var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/show?bib=https://jonmay.github.io/webpage/cutelabname/cutelabname.bib&group0=year&commas=true&theme=dividers&jsonp=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=https://jonmay.github.io/webpage/cutelabname/cutelabname.bib&group0=year&commas=true&theme=dividers&jsonp=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/show?bib=https://jonmay.github.io/webpage/cutelabname/cutelabname.bib&group0=year&commas=true&theme=dividers&jsonp=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
    2. \n
  2. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
    3. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2023\n \n \n (13)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Challenges in Context-Aware Neural Machine Translation.\n \n \n \n \n\n\n \n Jin, L., He, J., May, J., & Ma, X.\n\n\n \n\n\n\n In Bouamor, H., Pino, J., & Bali, K., editor(s), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 15246–15263, Singapore, December 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ChallengesPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{jin-etal-2023-challenges,\n    title = "Challenges in Context-Aware Neural Machine Translation",\n    author = "Jin, Linghao  and\n      He, Jacqueline  and\n      May, Jonathan  and\n      Ma, Xuezhe",\n    editor = "Bouamor, Houda  and\n      Pino, Juan  and\n      Bali, Kalika",\n    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",\n    month = dec,\n    year = "2023",\n    address = "Singapore",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.emnlp-main.943",\n    doi = "10.18653/v1/2023.emnlp-main.943",\n    pages = "15246--15263",\n    abstract = "Context-aware neural machine translation, a paradigm that involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, has given rise to a number of recent techniques. However, despite well-reasoned intuitions, most context-aware translation models show only modest improvements over sentence-level systems. In this work, we investigate and present several core challenges that impede progress within the field, relating to discourse phenomena, context usage, model architectures, and document-level evaluation. To address these problems, we propose a more realistic setting for document-level translation, called paragraph-to-paragraph (PARA2PARA) translation, and collect a new dataset of Chinese-English novels to promote future research.",\n}\n\n
\n
\n\n\n
\n Context-aware neural machine translation, a paradigm that involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, has given rise to a number of recent techniques. However, despite well-reasoned intuitions, most context-aware translation models show only modest improvements over sentence-level systems. In this work, we investigate and present several core challenges that impede progress within the field, relating to discourse phenomena, context usage, model architectures, and document-level evaluation. To address these problems, we propose a more realistic setting for document-level translation, called paragraph-to-paragraph (PARA2PARA) translation, and collect a new dataset of Chinese-English novels to promote future research.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Continual Dialogue State Tracking via Example-Guided Question Answering.\n \n \n \n \n\n\n \n Cho, H., Madotto, A., Lin, Z., Chandu, K., Kottur, S., Xu, J., May, J., & Sankar, C.\n\n\n \n\n\n\n In Bouamor, H., Pino, J., & Bali, K., editor(s), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3873–3886, Singapore, December 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ContinualPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{cho-etal-2023-continual,\n    title = "Continual Dialogue State Tracking via Example-Guided Question Answering",\n    author = "Cho, Hyundong  and\n      Madotto, Andrea  and\n      Lin, Zhaojiang  and\n      Chandu, Khyathi  and\n      Kottur, Satwik  and\n      Xu, Jing  and\n      May, Jonathan  and\n      Sankar, Chinnadhurai",\n    editor = "Bouamor, Houda  and\n      Pino, Juan  and\n      Bali, Kalika",\n    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",\n    month = dec,\n    year = "2023",\n    address = "Singapore",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.emnlp-main.235",\n    doi = "10.18653/v1/2023.emnlp-main.235",\n    pages = "3873--3886",\n    abstract = "Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services. Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user{'}s goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning. Our approach alleviates service-specific memorization and teaches a model to contextualize the given question and example to extract the necessary information from the conversation. We find that a model with just 60M parameters can achieve a significant boost by learning to learn from in-context examples retrieved by a retriever trained to identify turns with similar dialogue state changes. Combining our method with dialogue-level memory replay, our approach attains state of the art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods.",\n}\n\n
\n
\n\n\n
\n Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services. Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user's goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning. Our approach alleviates service-specific memorization and teaches a model to contextualize the given question and example to extract the necessary information from the conversation. We find that a model with just 60M parameters can achieve a significant boost by learning to learn from in-context examples retrieved by a retriever trained to identify turns with similar dialogue state changes. Combining our method with dialogue-level memory replay, our approach attains state of the art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Analyzing Norm Violations in Live-Stream Chat.\n \n \n \n \n\n\n \n Moon, J., Lee, D., Cho, H., Jin, W., Park, C., Kim, M., May, J., Pujara, J., & Park, S.\n\n\n \n\n\n\n In Bouamor, H., Pino, J., & Bali, K., editor(s), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 852–868, Singapore, December 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"AnalyzingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{moon-etal-2023-analyzing,\n    title = "Analyzing Norm Violations in Live-Stream Chat",\n    author = "Moon, Jihyung  and\n      Lee, Dong-Ho  and\n      Cho, Hyundong  and\n      Jin, Woojeong  and\n      Park, Chan  and\n      Kim, Minwoo  and\n      May, Jonathan  and\n      Pujara, Jay  and\n      Park, Sungjoon",\n    editor = "Bouamor, Houda  and\n      Pino, Juan  and\n      Bali, Kalika",\n    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",\n    month = dec,\n    year = "2023",\n    address = "Singapore",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.emnlp-main.55",\n    doi = "10.18653/v1/2023.emnlp-main.55",\n    pages = "852--868",\n    abstract = "Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35{\\%}.",\n}\n\n
\n
\n\n\n
\n Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35%.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Identifying Informational Sources in News Articles.\n \n \n \n \n\n\n \n Spangher, A., Peng, N., Ferrara, E., & May, J.\n\n\n \n\n\n\n In Bouamor, H., Pino, J., & Bali, K., editor(s), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3626–3639, Singapore, December 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"IdentifyingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{spangher-etal-2023-identifying,\n    title = "Identifying Informational Sources in News Articles",\n    author = "Spangher, Alexander  and\n      Peng, Nanyun  and\n      Ferrara, Emilio  and\n      May, Jonathan",\n    editor = "Bouamor, Houda  and\n      Pino, Juan  and\n      Bali, Kalika",\n    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",\n    month = dec,\n    year = "2023",\n    address = "Singapore",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.emnlp-main.221",\n    doi = "10.18653/v1/2023.emnlp-main.221",\n    pages = "3626--3639",\n    abstract = "News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing. We first show that our dataset can be used to train high-performing models for information detection and source attribution. Then, we introduce a novel task, source prediction, to study the compositionality of sources in news articles {--} i.e. how they are chosen to complement each other. We show good modeling performance on this task, indicating that there is a pattern to the way different sources are used \\textit{together} in news storytelling. This insight opens the door for a focus on sources in narrative science (i.e. planning-based language generation) and computational journalism (i.e. a source-recommendation system to aid journalists writing stories). All data and model code can be found at https://github.com/alex2awesome/source-exploration.",\n}\n\n
\n
\n\n\n
\n News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing. We first show that our dataset can be used to train high-performing models for information detection and source attribution. Then, we introduce a novel task, source prediction, to study the compositionality of sources in news articles – i.e. how they are chosen to complement each other. We show good modeling performance on this task, indicating that there is a pattern to the way different sources are used together in news storytelling. This insight opens the door for a focus on sources in narrative science (i.e. planning-based language generation) and computational journalism (i.e. a source-recommendation system to aid journalists writing stories). All data and model code can be found at https://github.com/alex2awesome/source-exploration.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Feedback Loops and Complex Dynamics of Harmful Speech in Online Discussions.\n \n \n \n \n\n\n \n Chang, R., May, J., & Lerman, K.\n\n\n \n\n\n\n In Social, Cultural, and Behavioral Modeling: 16th International Conference, SBP-BRiMS 2023, Pittsburgh, PA, USA, September 20–22, 2023, Proceedings, pages 85–94, Berlin, Heidelberg, 2023. Springer-Verlag\n \n\n\n\n
\n\n\n\n \n \n \"FeedbackPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1007/978-3-031-43129-6_9,\nauthor = {Chang, Rong-Ching and May, Jonathan and Lerman, Kristina},\ntitle = {Feedback Loops and&nbsp;Complex Dynamics of&nbsp;Harmful Speech in&nbsp;Online Discussions},\nyear = {2023},\nisbn = {978-3-031-43128-9},\npublisher = {Springer-Verlag},\naddress = {Berlin, Heidelberg},\nurl = {https://doi.org/10.1007/978-3-031-43129-6_9},\ndoi = {10.1007/978-3-031-43129-6_9},\nabstract = {Harmful and toxic speech contribute to an unwelcoming online environment that suppresses participation and conversation. Efforts have focused on detecting and mitigating harmful speech; however, the mechanisms by which toxicity degrades online discussions are not well understood. This paper makes two contributions. First, to comprehensively model harmful comments, we introduce a multilingual misogyny and sexist speech detection model (). Second, we model the complex dynamics of online discussions as feedback loops in which harmful comments lead to negative emotions which prompt even more harmful comments. To quantify the feedback loops, we use a combination of mutual Granger causality and regression to analyze discussions on two political forums on Reddit: the moderated political forum r/Politics and the moderated neutral political forum r/NeutralPolitics. Our results suggest that harmful comments and negative emotions create self-reinforcing feedback loops in forums. Contrarily, moderation with neutral discussion appears to tip interactions into self-extinguishing feedback loops that reduce harmful speech and negative emotions. Our study sheds more light on the complex dynamics of harmful speech and the role of moderation and neutral discussion in mitigating these dynamics.},\nbooktitle = {Social, Cultural, and Behavioral Modeling: 16th International Conference, SBP-BRiMS 2023, Pittsburgh, PA, USA, September 20–22, 2023, Proceedings},\npages = {85–94},\nnumpages = {10},\nkeywords = {Feedback Loop, Moderation, Granger Causality},\nlocation = {Pittsburgh, PA, USA}\n}\n\n
\n
\n\n\n
\n Harmful and toxic speech contribute to an unwelcoming online environment that suppresses participation and conversation. Efforts have focused on detecting and mitigating harmful speech; however, the mechanisms by which toxicity degrades online discussions are not well understood. This paper makes two contributions. First, to comprehensively model harmful comments, we introduce a multilingual misogyny and sexist speech detection model (). Second, we model the complex dynamics of online discussions as feedback loops in which harmful comments lead to negative emotions which prompt even more harmful comments. To quantify the feedback loops, we use a combination of mutual Granger causality and regression to analyze discussions on two political forums on Reddit: the moderated political forum r/Politics and the moderated neutral political forum r/NeutralPolitics. Our results suggest that harmful comments and negative emotions create self-reinforcing feedback loops in forums. Contrarily, moderation with neutral discussion appears to tip interactions into self-extinguishing feedback loops that reduce harmful speech and negative emotions. Our study sheds more light on the complex dynamics of harmful speech and the role of moderation and neutral discussion in mitigating these dynamics.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n First Steps Towards a Source Recommendation Engine: Investigating How Sources Are Used in News Articles.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n Zurich, Switzerland, June 2023.\n \n\n\n\n
\n\n\n\n \n \n \"FirstPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@Proceedings{spangher23.djc,\n  title =        {First Steps Towards a Source Recommendation Engine:\nInvestigating How Sources Are Used in News Articles},\n  year =         2023,\n  url={https://www.datajconf.com/papers/CJ_DataJConf_2023_paper_74.pdf},\n  booktitle = {Proc. The Joint Computation + Journalism European Data \\& Computational Journalism Conference},\n  address =   {Zurich, Switzerland},\n  month =     {June}}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Blend and Match: Distilling Semantic Search Models with Different Inductive Biases and Model Architectures.\n \n \n \n \n\n\n \n Bonab, H., Joshi, A., Bhatia, R., Gandhi, A., Huddar, V., Naik, J., Al-Darabsah, M., Teo, C. H., May, J., Agarwal, T., & Petricek, V.\n\n\n \n\n\n\n In Companion Proceedings of the ACM Web Conference 2023, of WWW '23 Companion, pages 869–877, New York, NY, USA, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"BlendPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3543873.3587629,\nauthor = {Bonab, Hamed and Joshi, Ashutosh and Bhatia, Ravi and Gandhi, Ankit and Huddar, Vijay and Naik, Juhi and Al-Darabsah, Mutasem and Teo, Choon Hui and May, Jonathan and Agarwal, Tarun and Petricek, Vaclav},\ntitle = {Blend and Match: Distilling Semantic Search Models with Different Inductive Biases and Model Architectures},\nyear = {2023},\nisbn = {9781450394192},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3543873.3587629},\ndoi = {10.1145/3543873.3587629},\nabstract = {Commercial search engines use different semantic models to augment lexical matches. These models provide candidate items for a user’s query from a target space of millions to billions of items. Models with different inductive biases provide relatively different predictions, making it desirable to launch multiple semantic models in production. However, latency and resource constraints make simultaneously deploying multiple models impractical. In this paper, we introduce a distillation approach, called Blend and Match (BM), to unify two different semantic search models into a single model. We use a Bi-encoder semantic matching model as our primary model and propose a novel loss function to incorporate eXtreme Multi-label Classification (XMC) predictions as the secondary model. Our experiments conducted on two large-scale datasets, collected from a popular e-commerce store, show that our proposed approach significantly improves the recall of the primary Bi-encoder model by 11\\% to 17\\% with a minimal loss in precision. We show that traditional knowledge distillation approaches result in a sub-optimal performance for our problem setting, and our BM approach yields comparable rankings with strong Rank Fusion (RF) methods used only if one could deploy multiple models.},\nbooktitle = {Companion Proceedings of the ACM Web Conference 2023},\npages = {869–877},\nnumpages = {9},\nkeywords = {Semantic Search, Ranking Distillation, Product Search, Model Blending},\nlocation = {Austin, TX, USA},\nseries = {WWW '23 Companion}\n}\n\n\n
\n
\n\n\n
\n Commercial search engines use different semantic models to augment lexical matches. These models provide candidate items for a user’s query from a target space of millions to billions of items. Models with different inductive biases provide relatively different predictions, making it desirable to launch multiple semantic models in production. However, latency and resource constraints make simultaneously deploying multiple models impractical. In this paper, we introduce a distillation approach, called Blend and Match (BM), to unify two different semantic search models into a single model. We use a Bi-encoder semantic matching model as our primary model and propose a novel loss function to incorporate eXtreme Multi-label Classification (XMC) predictions as the secondary model. Our experiments conducted on two large-scale datasets, collected from a popular e-commerce store, show that our proposed approach significantly improves the recall of the primary Bi-encoder model by 11% to 17% with a minimal loss in precision. We show that traditional knowledge distillation approaches result in a sub-optimal performance for our problem setting, and our BM approach yields comparable rankings with strong Rank Fusion (RF) methods used only if one could deploy multiple models.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Bridging the Gap between Native Text and Translated Text through Adversarial Learning: A Case Study on Cross-Lingual Event Extraction.\n \n \n \n \n\n\n \n Yu, P., May, J., & Ji, H.\n\n\n \n\n\n\n In Findings of the Association for Computational Linguistics: EACL 2023, pages 754–769, Dubrovnik, Croatia, May 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"BridgingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{yu-etal-2023-bridging,\n    title = "Bridging the Gap between Native Text and Translated Text through Adversarial Learning: A Case Study on Cross-Lingual Event Extraction",\n    author = "Yu, Pengfei  and\n      May, Jonathan  and\n      Ji, Heng",\n    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",\n    month = may,\n    year = "2023",\n    address = "Dubrovnik, Croatia",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.findings-eacl.57",\n    doi = "10.18653/v1/2023.findings-eacl.57",\n    pages = "754--769",\n    abstract = "Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance. We explore this idea for cross-lingual event extraction with a new model architecture that jointly encodes a source language input sentence with its translation to the target language during training, and takes a target language sentence with its translation back to the source language as input during evaluation. However, we observe significant representational gap between the native source language texts during training and the texts translated into source language during evaluation, as well as the texts translated into target language during training and the native target language texts during evaluation. This representational gap undermines the effectiveness of cross-lingual transfer learning for event extraction with machine-translated data. In order to mitigate this problem, we propose an adversarial training framework that encourages the language model to produce more similar representations for the translated text and the native text. To be specific, we train the language model such that its hidden representations are able to fool a jointly trained discriminator that distinguishes translated texts{'} representations from native texts{'} representations. We conduct experiments on cross-lingual for event extraction across three languages. Results demonstrate that our proposed adversarial training can effectively incorporate machine translation to improve event extraction, while simply adding machine-translated data yields unstable performance due to the representational gap.",\n}\n\n
\n
\n\n\n
\n Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance. We explore this idea for cross-lingual event extraction with a new model architecture that jointly encodes a source language input sentence with its translation to the target language during training, and takes a target language sentence with its translation back to the source language as input during evaluation. However, we observe significant representational gap between the native source language texts during training and the texts translated into source language during evaluation, as well as the texts translated into target language during training and the native target language texts during evaluation. This representational gap undermines the effectiveness of cross-lingual transfer learning for event extraction with machine-translated data. In order to mitigate this problem, we propose an adversarial training framework that encourages the language model to produce more similar representations for the translated text and the native text. To be specific, we train the language model such that its hidden representations are able to fool a jointly trained discriminator that distinguishes translated texts' representations from native texts' representations. We conduct experiments on cross-lingual for event extraction across three languages. Results demonstrate that our proposed adversarial training can effectively incorporate machine translation to improve event extraction, while simply adding machine-translated data yields unstable performance due to the representational gap.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation.\n \n \n \n \n\n\n \n Liu, S., Cho, H., Freedman, M., Ma, X., & May, J.\n\n\n \n\n\n\n In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8404–8419, Toronto, Canada, July 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"RECAP:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{liu-etal-2023-recap,\n    title = "{RECAP}: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation",\n    author = "Liu, Shuai  and\n      Cho, Hyundong  and\n      Freedman, Marjorie  and\n      Ma, Xuezhe  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = jul,\n    year = "2023",\n    address = "Toronto, Canada",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.acl-long.468",\n    doi = "10.18653/v1/2023.acl-long.468",\n    pages = "8404--8419",\n    abstract = "Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge. In this work, we propose a new retrieval-enhanced approach for personalized response generation. Specifically, we design a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. Extensive experiments on a real-world dataset demonstrate the effectiveness of our model at generating more fluent and personalized responses. We quantitatively evaluate our model{'}s performance under a suite of human and automatic metrics and find it to be superior compared to state-of-the-art baselines on English Reddit conversations.",\n}\n\n\n
\n
\n\n\n
\n Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge. In this work, we propose a new retrieval-enhanced approach for personalized response generation. Specifically, we design a hierarchical transformer retriever trained on dialogue domain data to perform personalized retrieval and a context-aware prefix encoder that fuses the retrieved information to the decoder more effectively. Extensive experiments on a real-world dataset demonstrate the effectiveness of our model at generating more fluent and personalized responses. We quantitatively evaluate our model's performance under a suite of human and automatic metrics and find it to be superior compared to state-of-the-art baselines on English Reddit conversations.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Anger Breeds Controversy: Analyzing Controversy and¬†Emotions on¬†Reddit.\n \n \n \n \n\n\n \n Chen, K., He, Z., Chang, R., May, J., & Lerman, K.\n\n\n \n\n\n\n In Thomson, R., Al-khateeb, S., Burger, A., Park, P., & A. Pyke, A., editor(s), Social, Cultural, and Behavioral Modeling, pages 44–53, Cham, 2023. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n \n \"AngerPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{10.1007/978-3-031-43129-6_5,\nauthor="Chen, Kai and He, Zihao and Chang, Rong-Ching and May, Jonathan and Lerman, Kristina",\neditor="Thomson, Robert and Al-khateeb, Samer and Burger, Annetta\nand Park, Patrick\nand A. Pyke, Aryn",\ntitle="Anger Breeds Controversy: Analyzing Controversy and Emotions on Reddit",\nbooktitle="Social, Cultural, and Behavioral Modeling",\nyear="2023",\npublisher="Springer Nature Switzerland",\naddress="Cham",\npages="44--53",\nurl = {https://arxiv.org/abs/2212.00339},\nabstract="Emotions play an important role in interpersonal interactions and social conflict, yet their function in the development of controversy and disagreement in online conversations has not been fully explored. To address this gap, we study controversy on Reddit, a popular network of online discussion forums. We collect discussions from various topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc. (Code and dataset are publicly available at https://github.com/ChenK7166/controversy-emotion). We find controversial comments express more anger and less admiration, joy, and optimism than non-controversial comments. Moreover, controversial comments affect emotions of downstream comments, resulting in a long-term increase in anger and a decrease in positive emotions. The magnitude and direction of emotional change differ by forum. Finally, we show that emotions help better predict which comments will become controversial. Understanding the dynamics of emotions in online discussions can help communities to manage conversations better.",\nisbn="978-3-031-43129-6"\n}\n\n\n
\n
\n\n\n
\n Emotions play an important role in interpersonal interactions and social conflict, yet their function in the development of controversy and disagreement in online conversations has not been fully explored. To address this gap, we study controversy on Reddit, a popular network of online discussion forums. We collect discussions from various topical forums and use emotion detection to recognize a range of emotions from text, including anger, fear, joy, admiration, etc. (Code and dataset are publicly available at https://github.com/ChenK7166/controversy-emotion). We find controversial comments express more anger and less admiration, joy, and optimism than non-controversial comments. Moreover, controversial comments affect emotions of downstream comments, resulting in a long-term increase in anger and a decrease in positive emotions. The magnitude and direction of emotional change differ by forum. Finally, we show that emotions help better predict which comments will become controversial. Understanding the dynamics of emotions in online discussions can help communities to manage conversations better.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Cross-lingual Continual Learning.\n \n \n \n \n\n\n \n M'hamdi, M., Ren, X., & May, J.\n\n\n \n\n\n\n In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3908–3943, Toronto, Canada, July 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Cross-lingualPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{mhamdi-etal-2023-cross,\n    title = "Cross-lingual Continual Learning",\n    author = "M{'}hamdi, Meryem  and\n      Ren, Xiang  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = jul,\n    year = "2023",\n    address = "Toronto, Canada",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.acl-long.217",\n    doi = "10.18653/v1/2023.acl-long.217",\n    pages = "3908--3943",\n    abstract = "The longstanding goal of multi-lingual learning has been to develop a universal cross-lingual model that can withstand the changes in multi-lingual data distributions. There has been a large amount of work to adapt such multi-lingual models to unseen target languages. However, the majority of work in this direction focuses on the standard one-hop transfer learning pipeline from source to target languages, whereas in realistic scenarios, new languages can be incorporated at any time in a sequential manner. In this paper, we present a principled Cross-lingual Continual Learning (CCL) evaluation paradigm, where we analyze different categories of approaches used to continually adapt to emerging data from different languages. We provide insights into what makes multilingual sequential learning particularly challenging. To surmount such challenges, we benchmark a representative set of cross-lingual continual learning algorithms and analyze their knowledge preservation, accumulation, and generalization capabilities compared to baselines on carefully curated datastreams. The implications of this analysis include a recipe for how to measure and balance different cross-lingual continual learning desiderata, which go beyond conventional transfer learning.",\n}\n\n
\n
\n\n\n
\n The longstanding goal of multi-lingual learning has been to develop a universal cross-lingual model that can withstand the changes in multi-lingual data distributions. There has been a large amount of work to adapt such multi-lingual models to unseen target languages. However, the majority of work in this direction focuses on the standard one-hop transfer learning pipeline from source to target languages, whereas in realistic scenarios, new languages can be incorporated at any time in a sequential manner. In this paper, we present a principled Cross-lingual Continual Learning (CCL) evaluation paradigm, where we analyze different categories of approaches used to continually adapt to emerging data from different languages. We provide insights into what makes multilingual sequential learning particularly challenging. To surmount such challenges, we benchmark a representative set of cross-lingual continual learning algorithms and analyze their knowledge preservation, accumulation, and generalization capabilities compared to baselines on carefully curated datastreams. The implications of this analysis include a recipe for how to measure and balance different cross-lingual continual learning desiderata, which go beyond conventional transfer learning.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning.\n \n \n \n \n\n\n \n Gheini, M., Ma, X., & May, J.\n\n\n \n\n\n\n In Findings of the Association for Computational Linguistics: ACL 2023, pages 11602–11612, Toronto, Canada, July 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"KnowPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{gheini-etal-2023-know,\n    title = "Know Where You{'}re Going: Meta-Learning for Parameter-Efficient Fine-Tuning",\n    author = "Gheini, Mozhdeh  and\n      Ma, Xuezhe  and\n      May, Jonathan",\n    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",\n    month = jul,\n    year = "2023",\n    address = "Toronto, Canada",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.findings-acl.737",\n    doi = "10.18653/v1/2023.findings-acl.737",\n    pages = "11602--11612",\n    abstract = "A recent family of techniques, dubbed lightweight fine-tuning methods, facilitates parameter-efficient transfer by updating only a small set of additional parameters while keeping the parameters of the original model frozen. While proven to be an effective approach, there are no existing studies on if and how such knowledge of the downstream fine-tuning approach calls for complementary measures after pre-training and before fine-tuning. In this work, we show that taking the ultimate choice of fine-tuning into consideration boosts the performance of parameter-efficient fine-tuning. By relying on optimization-based meta-learning using MAML with certain modifications for our distinct purpose, we prime the pre-trained model specifically for parameter-efficient fine-tuning, resulting in gains of up to 4.96 points on cross-lingual NER fine-tuning. Our ablation settings and analyses further reveal that the specific approach we take to meta-learning is crucial for the attained gains.",\n}\n\n
\n
\n\n\n
\n A recent family of techniques, dubbed lightweight fine-tuning methods, facilitates parameter-efficient transfer by updating only a small set of additional parameters while keeping the parameters of the original model frozen. While proven to be an effective approach, there are no existing studies on if and how such knowledge of the downstream fine-tuning approach calls for complementary measures after pre-training and before fine-tuning. In this work, we show that taking the ultimate choice of fine-tuning into consideration boosts the performance of parameter-efficient fine-tuning. By relying on optimization-based meta-learning using MAML with certain modifications for our distinct purpose, we prime the pre-trained model specifically for parameter-efficient fine-tuning, resulting in gains of up to 4.96 points on cross-lingual NER fine-tuning. Our ablation settings and analyses further reveal that the specific approach we take to meta-learning is crucial for the attained gains.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models.\n \n \n \n \n\n\n \n Felkner, V., Chang, H. H., Jang, E., & May, J.\n\n\n \n\n\n\n In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9126–9140, Toronto, Canada, July 2023. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"WinoQueer:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{felkner-etal-2023-winoqueer,\n    title = "{W}ino{Q}ueer: A Community-in-the-Loop Benchmark for Anti-{LGBTQ}+ Bias in Large Language Models",\n    author = "Felkner, Virginia  and\n      Chang, Ho-Chun Herbert  and\n      Jang, Eugene  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = jul,\n    year = "2023",\n    address = "Toronto, Canada",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.acl-long.507",\n    doi = "10.18653/v1/2023.acl-long.507",\n    pages = "9126--9140",\n    abstract = "We present WinoQueer: a benchmark specifically designed to measure whether large language models (LLMs) encode biases that are harmful to the LGBTQ+ community. The benchmark is community-sourced, via application of a novel method that generates a bias benchmark from a community survey. We apply our benchmark to several popular LLMs and find that off-the-shelf models generally do exhibit considerable anti-queer bias. Finally, we show that LLM bias against a marginalized community can be somewhat mitigated by finetuning on data written about or by members of that community, and that social media text written by community members is more effective than news text written about the community by non-members. Our method for community-in-the-loop benchmark development provides a blueprint for future researchers to develop community-driven, harms-grounded LLM benchmarks for other marginalized communities.",\n}\n\n\n
\n
\n\n\n
\n We present WinoQueer: a benchmark specifically designed to measure whether large language models (LLMs) encode biases that are harmful to the LGBTQ+ community. The benchmark is community-sourced, via application of a novel method that generates a bias benchmark from a community survey. We apply our benchmark to several popular LLMs and find that off-the-shelf models generally do exhibit considerable anti-queer bias. Finally, we show that LLM bias against a marginalized community can be somewhat mitigated by finetuning on data written about or by members of that community, and that social media text written by community members is more effective than news text written about the community by non-members. Our method for community-in-the-loop benchmark development provides a blueprint for future researchers to develop community-driven, harms-grounded LLM benchmarks for other marginalized communities.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2022\n \n \n (29)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics.\n \n \n \n \n\n\n \n Cho, H., Sankar, C., Lin, C., Sadagopan, K., Shayandeh, S., Celikyilmaz, A., May, J., & Beirami, A.\n\n\n \n\n\n\n In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5345–5359, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"KnowPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{cho-etal-2022-know,\n    title = "Know Thy Strengths: Comprehensive Dialogue State Tracking Diagnostics",\n    author = "Cho, Hyundong  and\n      Sankar, Chinnadhurai  and\n      Lin, Christopher  and\n      Sadagopan, Kaushik  and\n      Shayandeh, Shahin  and\n      Celikyilmaz, Asli  and\n      May, Jonathan  and\n      Beirami, Ahmad",\n    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",\n    month = dec,\n    year = "2022",\n    address = "Abu Dhabi, United Arab Emirates",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2022.findings-emnlp.391",\n    pages = "5345--5359",\n    abstract = "Recent works that revealed the vulnerability of dialogue state tracking (DST) models to distributional shifts have made holistic comparisons on robustness and qualitative analyses increasingly important for understanding their relative performance. We present our findings from standardized and comprehensive DST diagnoses, which have previously been sparse and uncoordinated, using our toolkit, CheckDST, a collection of robustness tests and failure mode analytics. We discover that different classes of DST models have clear strengths and weaknesses, where generation models are more promising for handling language variety while span-based classification models are more robust to unseen entities. Prompted by this discovery, we also compare checkpoints from the same model and find that the standard practice of selecting checkpoints using validation loss/accuracy is prone to overfitting and each model class has distinct patterns of failure. Lastly, we demonstrate how our diagnoses motivate a pre-finetuning procedure with non-dialogue data that offers comprehensive improvements to generation models by alleviating the impact of distributional shifts through transfer learning.",\n}\n\n\n
\n
\n\n\n
\n Recent works that revealed the vulnerability of dialogue state tracking (DST) models to distributional shifts have made holistic comparisons on robustness and qualitative analyses increasingly important for understanding their relative performance. We present our findings from standardized and comprehensive DST diagnoses, which have previously been sparse and uncoordinated, using our toolkit, CheckDST, a collection of robustness tests and failure mode analytics. We discover that different classes of DST models have clear strengths and weaknesses, where generation models are more promising for handling language variety while span-based classification models are more robust to unseen entities. Prompted by this discovery, we also compare checkpoints from the same model and find that the standard practice of selecting checkpoints using validation loss/accuracy is prone to overfitting and each model class has distinct patterns of failure. Lastly, we demonstrate how our diagnoses motivate a pre-finetuning procedure with non-dialogue data that offers comprehensive improvements to generation models by alleviating the impact of distributional shifts through transfer learning.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Checks and Strategies for Enabling Code-Switched Machine Translation.\n \n \n \n \n\n\n \n Gowda, T., Gheini, M., & May, J.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"ChecksPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2210.05096,\n  doi = {10.48550/ARXIV.2210.05096},\n  url = {https://arxiv.org/abs/2210.05096},\n  author = {Gowda, Thamme and Gheini, Mozhdeh and May, Jonathan},\n  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {Checks and Strategies for Enabling Code-Switched Machine Translation},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {Creative Commons Attribution Share Alike 4.0 International}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Mega: Moving Average Equipped Gated Attention.\n \n \n \n \n\n\n \n Ma, X., Zhou, C., Kong, X., He, J., Gui, L., Neubig, G., May, J., & Zettlemoyer, L.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"Mega:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2209.10655,\n  doi = {10.48550/ARXIV.2209.10655},\n  url = {https://arxiv.org/abs/2209.10655},\n  author = {Ma, Xuezhe and Zhou, Chunting and Kong, Xiang and He, Junxian and Gui, Liangke and Neubig, Graham and May, Jonathan and Zettlemoyer, Luke},\n  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {Mega: Moving Average Equipped Gated Attention},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {arXiv.org perpetual, non-exclusive license}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Building an Event Extractor with Only a Few Examples.\n \n \n \n \n\n\n \n Yu, P., Zhang, Z., Voss, C., May, & Heng Ji, J.\n\n\n \n\n\n\n In Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing, pages 102–109, Hybrid, July 2022. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"BuildingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{yu-etal-2022-building,\n    title = "Building an Event Extractor with Only a Few Examples",\n    author = "Yu, Pengfei  and\n      Zhang, Zixuan  and\n      Voss, Clare  and\n      May and Heng Ji, Jonathan",\n    booktitle = "Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing",\n    month = jul,\n    year = "2022",\n    address = "Hybrid",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2022.deeplo-1.11",\n    pages = "102--109",\n    abstract = "t",\n}\n\n
\n
\n\n\n
\n t\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores.\n \n \n \n \n\n\n \n Joshi, A., Vishwanath, S., Teo, C., Petricek, V., Vishwanathan, V., Bhagat, R., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pages 160–167, Hybrid: Seattle, Washington + Online, July 2022. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"AugmentingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{joshi-etal-2022-augmenting,\n    title = "Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic {E}-commerce Stores",\n    author = "Joshi, Ashutosh  and\n      Vishwanath, Shankar  and\n      Teo, Choon  and\n      Petricek, Vaclav  and\n      Vishwanathan, Vishy  and\n      Bhagat, Rahul  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",\n    month = jul,\n    year = "2022",\n    address = "Hybrid: Seattle, Washington + Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2022.naacl-industry.19",\n    pages = "160--167",\n    abstract = "Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amounts of customer behavior data (e.g. queries, clicks, purchases) for training. However, behavioral data is limited in low-traffic e-commerce stores, impacting performance of these systems. In this paper, we present a technique that augments behavioral training data via query reformulation. We use the Aggregated Label eXtreme Multi-label Classification (AL-XMC) system (Shen et al., 2020) as an example semantic matching model and show via crowd-sourced human judgments that, when the training data is augmented through query reformulations, the quality of AL-XMC improves over a baseline that does not use query reformulation. We also show in online A/B tests that our method significantly improves business metrics for the AL-XMC model.",\n}\n\n\n
\n
\n\n\n
\n Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amounts of customer behavior data (e.g. queries, clicks, purchases) for training. However, behavioral data is limited in low-traffic e-commerce stores, impacting performance of these systems. In this paper, we present a technique that augments behavioral training data via query reformulation. We use the Aggregated Label eXtreme Multi-label Classification (AL-XMC) system (Shen et al., 2020) as an example semantic matching model and show via crowd-sourced human judgments that, when the training data is augmented through query reformulations, the quality of AL-XMC improves over a baseline that does not use query reformulation. We also show in online A/B tests that our method significantly improves business metrics for the AL-XMC model.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Opponent Modeling in Negotiation Dialogues by Related Data Adaptation.\n \n \n \n \n\n\n \n Chawla, K., Lucas, G., May, J., & Gratch, J.\n\n\n \n\n\n\n In Findings of the Association for Computational Linguistics: NAACL 2022, pages 661–674, Seattle, United States, July 2022. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"OpponentPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chawla-etal-2022-opponent,\n    title = "Opponent Modeling in Negotiation Dialogues by Related Data Adaptation",\n    author = "Chawla, Kushal  and\n      Lucas, Gale  and\n      May, Jonathan  and\n      Gratch, Jonathan",\n    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",\n    month = jul,\n    year = "2022",\n    address = "Seattle, United States",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2022.findings-naacl.50",\n    pages = "661--674",\n    abstract = "Opponent modeling is the task of inferring another party{'}s mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent{'}s preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.",\n}\n\n\n\n
\n
\n\n\n
\n Opponent modeling is the task of inferring another party's mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent's preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Machine Translation Robustness to Natural Asemantic Variation.\n \n \n \n \n\n\n \n Bremerman, J., Ren, X., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3517–3532, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"MachinePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{bremerman-etal-2022-machine,\n    title = "Machine Translation Robustness to Natural Asemantic Variation",\n    author = "Bremerman, Jacob  and\n      Ren, Xiang  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",\n    month = dec,\n    year = "2022",\n    address = "Abu Dhabi, United Arab Emirates",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2022.emnlp-main.230",\n    pages = "3517--3532",\n    abstract = "Current Machine Translation (MT) models still struggle with more challenging input, such as noisy data and tail-end words and phrases. Several works have addressed this robustness issue by identifying specific categories of noise and variation then tuning models to perform better on them. An important yet under-studied category involves minor variations in nuance (non-typos) that preserve meaning w.r.t. the target language. We introduce and formalize this category as Natural Asemantic Variation (NAV) and investigate it in the context of MT robustness. We find that existing MT models fail when presented with NAV data, but we demonstrate strategies to improve performance on NAV by fine-tuning them with human-generated variations. We also show that NAV robustness can be transferred across languages and find that synthetic perturbations can achieve some but not all of the benefits of organic NAV data.",\n}\n\n\n
\n
\n\n\n
\n Current Machine Translation (MT) models still struggle with more challenging input, such as noisy data and tail-end words and phrases. Several works have addressed this robustness issue by identifying specific categories of noise and variation then tuning models to perform better on them. An important yet under-studied category involves minor variations in nuance (non-typos) that preserve meaning w.r.t. the target language. We introduce and formalize this category as Natural Asemantic Variation (NAV) and investigate it in the context of MT robustness. We find that existing MT models fail when presented with NAV data, but we demonstrate strategies to improve performance on NAV by fine-tuning them with human-generated variations. We also show that NAV robustness can be transferred across languages and find that synthetic perturbations can achieve some but not all of the benefits of organic NAV data.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n NewsEdits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge.\n \n \n \n \n\n\n \n Spangher, A., Ren, X., May, J., & Peng, N.\n\n\n \n\n\n\n In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 127–157, Seattle, United States, July 2022. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"NewsEdits:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{spangher-etal-2022-newsedits,\n    title = "{N}ews{E}dits: A News Article Revision Dataset and a Novel Document-Level Reasoning Challenge",\n    author = "Spangher, Alexander  and\n      Ren, Xiang  and\n      May, Jonathan  and\n      Peng, Nanyun",\n    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",\n    month = jul,\n    year = "2022",\n    address = "Seattle, United States",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2022.naacl-main.10",\n    pages = "127--157",\n    abstract = "News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspaper sources based in three countries, spanning 15 years of coverage (2006-2021).We define article-level edit actions: Addition, Deletion, Edit and Refactor, and develop a high-accuracy extraction algorithm to identify these actions. To underscore the factual nature of many edit actions, we conduct analyses showing that added and deleted sentences are more likely to contain updating events, main content and quotes than unchanged sentences. Finally, to explore whether edit actions are predictable, we introduce three novel tasks aimed at predicting actions performed during version updates. We show that these tasks are possible for expert humans but are challenging for large NLP models. We hope this can spur research in narrative framing and help provide predictive tools for journalists chasing breaking news.",\n}\n\n\n\n
\n
\n\n\n
\n News article revision histories provide clues to narrative and factual evolution in news articles. To facilitate analysis of this evolution, we present the first publicly available dataset of news revision histories, NewsEdits. Our dataset is large-scale and multilingual; it contains 1.2 million articles with 4.6 million versions from over 22 English- and French-language newspaper sources based in three countries, spanning 15 years of coverage (2006-2021).We define article-level edit actions: Addition, Deletion, Edit and Refactor, and develop a high-accuracy extraction algorithm to identify these actions. To underscore the factual nature of many edit actions, we conduct analyses showing that added and deleted sentences are more likely to contain updating events, main content and quotes than unchanged sentences. Finally, to explore whether edit actions are predictable, we introduce three novel tasks aimed at predicting actions performed during version updates. We show that these tasks are possible for expert humans but are challenging for large NLP models. We hope this can spur research in narrative framing and help provide predictive tools for journalists chasing breaking news.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Towards WinoQueer: Developing a Benchmark for Anti-Queer Bias in Large Language Models.\n \n \n \n \n\n\n \n Felkner, V. K., Chang, H. H., Jang, E., & May, J.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2206.11484,\n  doi = {10.48550/ARXIV.2206.11484},\n  url = {https://arxiv.org/abs/2206.11484},\n  author = {Felkner, Virginia K. and Chang, Ho-Chun Herbert and Jang, Eugene and May, Jonathan},\n  keywords = {Computation and Language (cs.CL), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7},\n  title = {Towards WinoQueer: Developing a Benchmark for Anti-Queer Bias in Large Language Models},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {Creative Commons Attribution 4.0 International}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Segmenting Numerical Substitution Ciphers.\n \n \n \n \n\n\n \n Aldarrab, N., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 706–714, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"SegmentingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{aldarrab-may-2022-segmenting,\n    title = "Segmenting Numerical Substitution Ciphers",\n    author = "Aldarrab, Nada  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",\n    month = dec,\n    year = "2022",\n    address = "Abu Dhabi, United Arab Emirates",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2022.emnlp-main.44",\n    pages = "706--714",\n    abstract = "Deciphering historical substitution ciphers is a challenging problem. Example problems that have been previously studied include detecting cipher type, detecting plaintext language, and acquiring the substitution key for segmented ciphers. However, attacking unsegmented ciphers is still a challenging task. Segmentation (i.e. finding substitution units) is essential for cracking those ciphers. In this work, we propose the first automatic methods to segment those ciphers using Byte Pair Encoding (BPE) and unigram language models. Our methods achieve an average segmentation error of 2{\\%} on 100 randomly-generated monoalphabetic ciphers and 27{\\%} on 3 real historical homophonic ciphers. We also propose a method for solving non-deterministic ciphers with existing keys using a lattice and a pretrained language model. Our method leads to the full solution of the IA cipher; a real historical cipher that has not been fully solved until this work.",\n}\n\n
\n
\n\n\n
\n Deciphering historical substitution ciphers is a challenging problem. Example problems that have been previously studied include detecting cipher type, detecting plaintext language, and acquiring the substitution key for segmented ciphers. However, attacking unsegmented ciphers is still a challenging task. Segmentation (i.e. finding substitution units) is essential for cracking those ciphers. In this work, we propose the first automatic methods to segment those ciphers using Byte Pair Encoding (BPE) and unigram language models. Our methods achieve an average segmentation error of 2% on 100 randomly-generated monoalphabetic ciphers and 27% on 3 real historical homophonic ciphers. We also propose a method for solving non-deterministic ciphers with existing keys using a lattice and a pretrained language model. Our method leads to the full solution of the IA cipher; a real historical cipher that has not been fully solved until this work.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Towards a Unified View of Parameter-Efficient Transfer Learning.\n \n \n \n\n\n \n He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., & Neubig, G.\n\n\n \n\n\n\n In Proceedings of the 10th International Conference on Learning Representations (ICLR-2022), 2022. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{he2022towards,\n  title={Towards a Unified View of Parameter-Efficient Transfer Learning},\n  author={He, Junxian and Zhou, Chunting and Ma, Xuezhe and Berg-Kirkpatrick, Taylor and Neubig, Graham},\n  booktitle={Proceedings of the 10th International Conference on Learning Representations (ICLR-2022)},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Ultra-fine entity typing with indirect supervision from natural language inference.\n \n \n \n\n\n \n Li, B., Yin, W., & Chen, M.\n\n\n \n\n\n\n Transactions of the Association for Computational Linguistics, 10: 607–622. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{li2022ultra,\n  title={Ultra-fine entity typing with indirect supervision from natural language inference},\n  author={Li, Bangzheng and Yin, Wenpeng and Chen, Muhao},\n  journal={Transactions of the Association for Computational Linguistics},\n  volume={10},\n  pages={607--622},\n  year={2022},\n  publisher={MIT Press}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n New Frontiers of Information Extraction.\n \n \n \n\n\n \n Chen, M., Huang, L., Li, M., Zhou, B., Ji, H., & Roth, D.\n\n\n \n\n\n\n In NAACL, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chen2022ie-naacl,\n  title={New Frontiers of Information Extraction},\n  author={Chen, Muhao and Huang, Lifu and Li, Manling and Zhou, Ben and Ji, Heng and Roth, Dan},\n  booktitle={NAACL},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning.\n \n \n \n\n\n \n Wang, F., Xu, Z., Szekely, P., & Chen, M.\n\n\n \n\n\n\n NAACL. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{wang2022robust,\n  title={Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning},\n  author={Wang, Fei and Xu, Zhewei and Szekely, Pedro and Chen, Muhao},\n  journal={NAACL},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis.\n \n \n \n\n\n \n Wang, Y., Chen, M., Zhou, W., Cai, Y., Liang, Y., Liu, D., Yang, B., Liu, J., & Hooi, B.\n\n\n \n\n\n\n NAACL. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{wang2022should,\n  title={Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis},\n  author={Wang, Yiwei and Chen, Muhao and Zhou, Wenxuan and Cai, Yujun and Liang, Yuxuan and Liu, Dayiheng and Yang, Baosong and Liu, Juncheng and Hooi, Bryan},\n  journal={NAACL},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Answer Consolidation: Formulation and Benchmarking.\n \n \n \n\n\n \n Zhou, W., Ning, Q., Elfardy, H., Small, K., & Chen, M.\n\n\n \n\n\n\n NAACL. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{zhou2022answer,\n  title={Answer Consolidation: Formulation and Benchmarking},\n  author={Zhou, Wenxuan and Ning, Qiang and Elfardy, Heba and Small, Kevin and Chen, Muhao},\n  journal={NAACL},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Unified Semantic Typing with Meaningful Label Inference.\n \n \n \n\n\n \n Huang, J. Y, Li, B., Xu, J., & Chen, M.\n\n\n \n\n\n\n NAACL. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{huang2022unified,\n  title={Unified Semantic Typing with Meaningful Label Inference},\n  author={Huang, James Y and Li, Bangzheng and Xu, Jiashu and Chen, Muhao},\n  journal={NAACL},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Dangling-Aware Entity Alignment with Mixed High-Order Proximities.\n \n \n \n\n\n \n Liu, J., Sun, Z., Hooi, B., Wang, Y., Liu, D., Yang, B., Xiao, X., & Chen, M.\n\n\n \n\n\n\n NAACL - Findings. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{liu2022dangling,\n  title={Dangling-Aware Entity Alignment with Mixed High-Order Proximities},\n  author={Liu, Juncheng and Sun, Zequn and Hooi, Bryan and Wang, Yiwei and Liu, Dayiheng and Yang, Baosong and Xiao, Xiaokui and Chen, Muhao},\n  journal={NAACL - Findings},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction.\n \n \n \n\n\n \n Wang, Y., Chen, M., Zhou, W., Cai, Y., Liang, Y., & Hooi, B.\n\n\n \n\n\n\n NAACL - Findings. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{wang2022graphcache,\n  title={GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction},\n  author={Wang, Yiwei and Chen, Muhao and Zhou, Wenxuan and Cai, Yujun and Liang, Yuxuan and Hooi, Bryan},\n  journal={NAACL - Findings},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Prix-LM: Pretraining for Multilingual Knowledge Base Construction.\n \n \n \n\n\n \n Zhou, W., Liu, F., Vulić, I., Collier, N., & Chen, M.\n\n\n \n\n\n\n In ACL, pages 5412–5424, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{zhou2022prix,\n  title={Prix-LM: Pretraining for Multilingual Knowledge Base Construction},\n  author={Zhou, Wenxuan and Liu, Fangyu and Vuli{\\'c}, Ivan and Collier, Nigel and Chen, Muhao},\n  booktitle={ACL},\n  pages={5412--5424},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Contextualized Scene Imagination for Generative Commonsense Reasoning.\n \n \n \n\n\n \n Wang, P., Zamora, J., Liu, J., Liu, F., Chen, M., & Ren, X.\n\n\n \n\n\n\n In ICLR, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{wang2022contextualized,\n  title={Contextualized Scene Imagination for Generative Commonsense Reasoning},\n  author={Wang, PeiFeng and Zamora, Jonathan and Liu, Junfeng and Liu, F. and Chen, Muhao and Ren, Xiang},\n  booktitle={ICLR},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n SOSum: A Dataset of Stack Overflow Post Summaries.\n \n \n \n\n\n \n Kou, B., Di, Y., Chen, M., & Zhang, T.\n\n\n \n\n\n\n In MSR, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{kousosum,\n  title={SOSum: A Dataset of Stack Overflow Post Summaries},\n  author={Kou, Bonan and Di, Yifeng and Chen, Muhao and Zhang, Tianyi},\n  booktitle={MSR},\n  year={2022}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Opponent Modeling in Negotiation Dialogues by Related Data Adaptation.\n \n \n \n \n\n\n \n Chawla, K., Lucas, G. M., May, J., & Gratch, J.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"OpponentPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2205.00344,\n  doi = {10.48550/ARXIV.2205.00344},\n  url = {https://arxiv.org/abs/2205.00344},\n  author = {Chawla, Kushal and Lucas, Gale M. and May, Jonathan and Gratch, Jonathan},\n  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {Opponent Modeling in Negotiation Dialogues by Related Data Adaptation},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {Creative Commons Attribution 4.0 International}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Cross-lingual Lifelong Learning.\n \n \n \n \n\n\n \n M'hamdi, M., Ren, X., & May, J.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"Cross-lingualPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2205.11152,\n  doi = {10.48550/ARXIV.2205.11152},\n  url = {https://arxiv.org/abs/2205.11152},\n  author = {M'hamdi, Meryem and Ren, Xiang and May, Jonathan},\n  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {Cross-lingual Lifelong Learning},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {Creative Commons Attribution 4.0 International}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-tuning.\n \n \n \n \n\n\n \n Gheini, M., Ma, X., & May, J.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"KnowPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2205.12453,\n  doi = {10.48550/ARXIV.2205.12453},\n  url = {https://arxiv.org/abs/2205.12453},\n  author = {Gheini, Mozhdeh and Ma, Xuezhe and May, Jonathan},\n  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-tuning},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {Creative Commons Attribution 4.0 International}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Machine Translation Robustness to Natural Asemantic Variation.\n \n \n \n \n\n\n \n Bremerman, J., Ren, X., & May, J.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"MachinePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2205.12514,\n  doi = {10.48550/ARXIV.2205.12514},\n  url = {https://arxiv.org/abs/2205.12514},\n  author = {Bremerman, Jacob and Ren, Xiang and May, Jonathan},\n  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {Machine Translation Robustness to Natural Asemantic Variation},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {Creative Commons Attribution 4.0 International}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge.\n \n \n \n \n\n\n \n Spangher, A., Ren, X., May, J., & Peng, N.\n\n\n \n\n\n\n . 2022.\n \n\n\n\n
\n\n\n\n \n \n \"NewsEdits:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{https://doi.org/10.48550/arxiv.2206.07106,\n  doi = {10.48550/ARXIV.2206.07106},\n  url = {https://arxiv.org/abs/2206.07106},\n  author = {Spangher, Alexander and Ren, Xiang and May, Jonathan and Peng, Nanyun},\n  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {Creative Commons Attribution 4.0 International}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Investigating the Benefits of Free-Form Rationales.\n \n \n \n \n\n\n \n Sun, J., Swayamdipta, S., May, J., & Ma, X.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"InvestigatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2206.11083,\n  doi = {10.48550/ARXIV.2206.11083},\n  url = {https://arxiv.org/abs/2206.11083},\n  author = {Sun, Jiao and Swayamdipta, Swabha and May, Jonathan and Ma, Xuezhe},\n  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {Investigating the Benefits of Free-Form Rationales},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {Creative Commons Attribution 4.0 International}\n}\n\n\n\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Segmenting Numerical Substitution Ciphers.\n \n \n \n \n\n\n \n Aldarrab, N., & May, J.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
\n\n\n\n \n \n \"SegmentingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2205.12527,\n  doi = {10.48550/ARXIV.2205.12527},\n  url = {https://arxiv.org/abs/2205.12527},\n  author = {Aldarrab, Nada and May, Jonathan},\n  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {Segmenting Numerical Substitution Ciphers},\n  publisher = {arXiv},\n  year = {2022},\n  copyright = {arXiv.org perpetual, non-exclusive license}\n}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2021\n \n \n (39)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n CheckDST: Measuring Real-World Generalization of Dialogue State Tracking Performance.\n \n \n \n \n\n\n \n Cho, H., Sankar, C., Lin, C., Sadagopan, K. R., Shayandeh, S., Celikyilmaz, A., May, J., & Beirami, A.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
\n\n\n\n \n \n \"CheckDST:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@misc{https://doi.org/10.48550/arxiv.2112.08321,\n  doi = {10.48550/ARXIV.2112.08321},\n  url = {https://arxiv.org/abs/2112.08321},\n  author = {Cho, Hyundong and Sankar, Chinnadhurai and Lin, Christopher and Sadagopan, Kaushik Ram and Shayandeh, Shahin and Celikyilmaz, Asli and May, Jonathan and Beirami, Ahmad},\n  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},\n  title = {CheckDST: Measuring Real-World Generalization of Dialogue State Tracking Performance},\n  publisher = {arXiv},\n  year = {2021},\n  copyright = {Creative Commons Attribution 4.0 International}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Luna: Linear unified nested attention.\n \n \n \n\n\n \n Ma, X., Kong, X., Wang, S., Zhou, C., May, J., Ma, H., & Zettlemoyer, L.\n\n\n \n\n\n\n Advances in Neural Information Processing Systems, 34: 2441–2453. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ma2021luna,\n  title={Luna: Linear unified nested attention},\n  author={Ma, Xuezhe and Kong, Xiang and Wang, Sinong and Zhou, Chunting and May, Jonathan and Ma, Hao and Zettlemoyer, Luke},\n  journal={Advances in Neural Information Processing Systems},\n  volume={34},\n  pages={2441--2453},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n AESOP: Paraphrase Generation with Adaptive Syntactic Control.\n \n \n \n\n\n \n Sun, J., Ma, X., & Peng, N.\n\n\n \n\n\n\n In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP-2021), Punta Cana, Dominican Republic, November 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{sun2021aesop,\n    title = {AESOP: Paraphrase Generation with Adaptive Syntactic Control},\n    author = {Sun, Jiao and Ma, Xuezhe and Peng, Nanyun},\n    booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP-2021)},\n    address = {Punta Cana, Dominican Republic},\n    month = {November},\n    year = {2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Decoupling Global and Local Representations via Invertible Generative Flows.\n \n \n \n\n\n \n Ma, X., Kong, X., Zhang, S., & Hovy, E.\n\n\n \n\n\n\n In Proceedings of the 9th International Conference on Learning Representations (ICLR-2021), May 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{decoupling2021,\n  title = {Decoupling Global and Local Representations via Invertible Generative Flows},\n  author = {Ma, Xuezhe and Kong, Xiang and Zhang, Shanghang and Hovy, Eduard},\n  booktitle = {Proceedings of the 9th International Conference on Learning Representations (ICLR-2021)},\n  year = {2021},\n  month = {May},\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Examining and Combating Spurious Features under Distribution Shift.\n \n \n \n\n\n \n Zhou, C., Ma, X., Michel, P., & Neubig, G.\n\n\n \n\n\n\n In Meila, M., & Zhang, T., editor(s), Proceedings of the 38th International Conference on Machine Learning (ICML-2021), volume 139, of Proceedings of Machine Learning Research, pages 12857–12867, 18–24 Jul 2021. PMLR\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{pmlr-v139-zhou21g,\n  title =     {Examining and Combating Spurious Features under Distribution Shift},\n  author =    {Zhou, Chunting and Ma, Xuezhe and Michel, Paul and Neubig, Graham},\n  booktitle = {Proceedings of the 38th International Conference on Machine Learning (ICML-2021)},\n  pages =     {12857--12867},\n  year =    {2021},\n  editor =    {Meila, Marina and Zhang, Tong},\n  volume =    {139},\n  series =    {Proceedings of Machine Learning Research},\n  month =     {18--24 Jul},\n  publisher = {PMLR},\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences.\n \n \n \n\n\n \n Singh, S., Wen, N., Hou, Y., Alipoormolabashi, P., Wu, T., Ma, X., & Peng, N.\n\n\n \n\n\n\n In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 883–898, August 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{singh-etal-2021-com2sense,\n    title = "{COM}2{SENSE}: A Commonsense Reasoning Benchmark with Complementary Sentences",\n    author = "Singh, Shikhar  and\n      Wen, Nuan  and\n      Hou, Yu  and\n      Alipoormolabashi, Pegah  and\n      Wu, Te-lin  and\n      Ma, Xuezhe  and\n      Peng, Nanyun",\n    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",\n    month = August,\n    year = "2021",\n    publisher = "Association for Computational Linguistics",\n    pages = "883--898",\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Learning from Noisy Labels for Entity-Centric Information Extraction.\n \n \n \n\n\n \n Zhou, W., & Chen, M.\n\n\n \n\n\n\n In EMNLP, pages 5381–5392, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{zhou2021learning,\n  title={Learning from Noisy Labels for Entity-Centric Information Extraction},\n  author={Zhou, Wenxuan and Chen, Muhao},\n  booktitle={EMNLP},\n  pages={5381--5392},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Learning Constraints and Descriptive Segmentation for Subevent Detection.\n \n \n \n\n\n \n Wang, H., Zhang, H., Chen, M., & Roth, D.\n\n\n \n\n\n\n In EMNLP, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{wang2021learning,\n  title={Learning Constraints and Descriptive Segmentation for Subevent Detection},\n  author={Wang, Haoyu and Zhang, Hongming and Chen, Muhao and Roth, Dan},\n  booktitle={EMNLP},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Contrastive Out-of-Distribution Detection for Pretrained Transformers.\n \n \n \n\n\n \n Zhou, W., Liu, F., & Chen, M.\n\n\n \n\n\n\n In EMNLP, pages 1100–1111, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{zhou2021contrastive,\n  title={Contrastive Out-of-Distribution Detection for Pretrained Transformers},\n  author={Zhou, Wenxuan and Liu, Fangyu and Chen, Muhao},\n  booktitle={EMNLP},\n  pages={1100--1111},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Table-based Fact Verification With Salience-aware Learning.\n \n \n \n\n\n \n Wang, F., Sun, K., Pujara, J., Szekely, P. A, & Chen, M.\n\n\n \n\n\n\n In EMNLP - Findings, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{wang2021table,\n  title={Table-based Fact Verification With Salience-aware Learning},\n  author={Wang, Fei and Sun, Kexuan and Pujara, Jay and Szekely, Pedro A and Chen, Muhao},\n  booktitle={EMNLP - Findings},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning.\n \n \n \n\n\n \n Ma, M. D., Chen, M., Wu, T., & Peng, N.\n\n\n \n\n\n\n In EMNLP - Findings, pages 4182–4194, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{ma2021hyperexpan,\n  title={HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning},\n  author={Ma, Mingyu Derek and Chen, Muhao and Wu, Te-Lin and Peng, Nanyun},\n  booktitle={EMNLP - Findings},\n  pages={4182--4194},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Event-Centric Natural Language Processing.\n \n \n \n\n\n \n Chen, M., Zhang, H., Ning, Q., Li, M., Ji, H., McKeown, K., & Roth, D.\n\n\n \n\n\n\n In ACL, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chen2021event,\n  title={Event-Centric Natural Language Processing},\n  author={Chen, Muhao and Zhang, Hongming and Ning, Qiang and Li, Manling and Ji, Heng and McKeown, Kathleen and Roth, Dan},\n  booktitle={ACL},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Knowing the No-match: Entity Alignment with Dangling Cases.\n \n \n \n\n\n \n Sun, Z., Chen, M., & Hu, W.\n\n\n \n\n\n\n In ACL, pages 3582–3593, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{sun2021knowing,\n  title={Knowing the No-match: Entity Alignment with Dangling Cases},\n  author={Sun, Zequn and Chen, Muhao and Hu, Wei},\n  booktitle={ACL},\n  pages={3582--3593},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Do Language Models Perform Generalizable Commonsense Inference?.\n \n \n \n\n\n \n Wang, P., Ilievski, F., Chen, M., & Ren, X.\n\n\n \n\n\n\n In ACL - Findings, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{wang2021language,\n  title={Do Language Models Perform Generalizable Commonsense Inference?},\n  author={Wang, Peifeng and Ilievski, Filip and Chen, Muhao and Ren, Xiang},\n  booktitle={ACL - Findings},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning.\n \n \n \n\n\n \n Chen, X., Boratko, M., Chen, M., Dasgupta, S. S., Li, X. L., & McCallum, A.\n\n\n \n\n\n\n In NAACL, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chen2021probabilistic,\n  title={Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning},\n  author={Chen, Xuelu and Boratko, Michael and Chen, Muhao and Dasgupta, Shib Sankar and Li, Xiang Lorraine and McCallum, Andrew},\n  booktitle={NAACL},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Event-Centric Natural Language Understanding.\n \n \n \n\n\n \n Chen, M., Zhang, H., Ning, Q., Li, M., Ji, H., & Roth, D.\n\n\n \n\n\n\n AAAI Tutorials. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{chen2021event,\n  title={Event-Centric Natural Language Understanding},\n  author={Chen, Muhao and Zhang, Hongming and Ning, Qiang and Li, Manling and Ji, Heng and Roth, Dan},\n  journal={AAAI Tutorials},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Visual Pivoting for (Unsupervised) Entity Alignment.\n \n \n \n\n\n \n Liu, F., Chen, M., Roth, D., & Collier, N.\n\n\n \n\n\n\n In AAAI, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{liu2021visual,\n  title={Visual Pivoting for (Unsupervised) Entity Alignment},\n  author={Liu, Fangyu and Chen, Muhao and Roth, Dan and Collier, Nigel},\n  booktitle={AAAI},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks.\n \n \n \n\n\n \n Zhu, C., Chen, M., Fan, C., Cheng, G., & Zhang, Y.\n\n\n \n\n\n\n In AAAI, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{zhu2021learning,\n  title={Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks},\n  author={Zhu, Cunchao and Chen, Muhao and Fan, Changjun and Cheng, Guangquan and Zhang, Yan},\n  booktitle={AAAI},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Cross-lingual Entity Alignment with Incidental Supervision.\n \n \n \n\n\n \n Chen, M., Shi, W., Zhou, B., & Roth, D.\n\n\n \n\n\n\n In EACL, pages 645–658, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chen2021cross,\n  title={Cross-lingual Entity Alignment with Incidental Supervision},\n  author={Chen, Muhao and Shi, Weijia and Zhou, Ben and Roth, Dan},\n  booktitle={EACL},\n  pages={645--658},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n JEDI: circular RNA prediction based on junction encoders and deep interaction among splice sites.\n \n \n \n\n\n \n Jiang, J., Ju, C. J., Hao, J., Chen, M., & Wang, W.\n\n\n \n\n\n\n Bioinformatics, 37(Supplement_1): i289–i298. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{jiang2021jedi,\n  title={JEDI: circular RNA prediction based on junction encoders and deep interaction among splice sites},\n  author={Jiang, Jyun-Yu and Ju, Chelsea J-T and Hao, Junheng and Chen, Muhao and Wang, Wei},\n  journal={Bioinformatics},\n  volume={37},\n  number={Supplement\\_1},\n  pages={i289--i298},\n  year={2021},\n  publisher={Oxford University Press}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables.\n \n \n \n\n\n \n Pham, M., Knoblock, C. A, Chen, M., Vu, B., & Pujara, J.\n\n\n \n\n\n\n In IJCAI, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{pham2021spade,\n  title={SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables.},\n  author={Pham, Minh and Knoblock, Craig A and Chen, Muhao and Vu, Binh and Pujara, Jay},\n  booktitle={IJCAI},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Tabular Functional Block Detection with Embedding-based Agglomerative Cell Clustering.\n \n \n \n\n\n \n Sun, K., Wang, F., Chen, M., & Pujara, J.\n\n\n \n\n\n\n In CIKM, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{sun2021tabular,\n  title={Tabular Functional Block Detection with Embedding-based Agglomerative Cell Clustering},\n  author={Sun, Kexuan and Wang, Fei and Chen, Muhao and Pujara, Jay},\n  booktitle={CIKM},\n  year={2021}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n StateCensusLaws.org: A Web Application for Consuming and Annotating Legal Discourse Learning.\n \n \n \n \n\n\n \n Spangher, A., & May, J.\n\n\n \n\n\n\n CoRR, abs/2104.10263. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"StateCensusLaws.org:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/abs-2104-10263,\n  author    = {Alexander Spangher and\n               Jonathan May},\n  title     = {StateCensusLaws.org: A Web Application\n               for Consuming and Annotating Legal Discourse Learning},\n  journal   = {CoRR},\n  volume    = {abs/2104.10263},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2104.10263},\n  eprinttype = {arXiv},\n  eprint    = {2104.10263},\n  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-10263.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Viola: A Topic Agnostic Generate-and-Rank Dialogue System.\n \n \n \n \n\n\n \n Cho, H., Shbita, B., Shenoy, K., Liu, S., Patel, N., Pindikanti, H., Lee, J., & May, J.\n\n\n \n\n\n\n CoRR, abs/2108.11063. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Viola:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/abs-2108-11063,\n  author    = {Hyundong Cho and\n               Basel Shbita and\n               Kartik Shenoy and\n               Shuai Liu and\n               Nikhil Patel and\n               Hitesh Pindikanti and\n               Jennifer Lee and\n               Jonathan May},\n  title     = {Viola: {A} Topic Agnostic Generate-and-Rank Dialogue System},\n  journal   = {CoRR},\n  volume    = {abs/2108.11063},\n  year      = {2021},\n  url       = {https://arxiv.org/abs/2108.11063},\n  eprinttype = {arXiv},\n  eprint    = {2108.11063},\n  timestamp = {Fri, 27 Aug 2021 15:02:29 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-2108-11063.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Explaining Face Presentation Attack Detection Using Natural Language.\n \n \n \n\n\n \n Mirzaalian, H., Hussein, M. E., Spinoulas, L., May, J., & Abd-Almageed, W.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{mirzaalian2021explaining,\n      title={Explaining Face Presentation Attack Detection Using Natural Language}, \n      author={Hengameh Mirzaalian and Mohamed E. Hussein and Leonidas Spinoulas and Jonathan May and Wael Abd-Almageed},\n      year={2021},\n      eprint={2111.04862},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n PERFUME: Programmatic Extraction and Refinement for Usability of Mathematical Expression.\n \n \n \n \n\n\n \n Weideman, N., Felkner, V. K., Wu, W., May, J., Hauser, C., & Garcia, L.\n\n\n \n\n\n\n In Proceedings of the 2021 Research on Offensive and Defensive Techniques in the Context of Man At The End (MATE) Attacks, of Checkmate '21, pages 59–69, New York, NY, USA, 2021. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"PERFUME:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{10.1145/3465413.3488575,\nauthor = {Weideman, Nicolaas and Felkner, Virginia K. and Wu, Wei-Cheng and May, Jonathan and Hauser, Christophe and Garcia, Luis},\ntitle = {PERFUME: Programmatic Extraction and Refinement for Usability of Mathematical Expression},\nyear = {2021},\nisbn = {9781450385527},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3465413.3488575},\ndoi = {10.1145/3465413.3488575},\nabstract = {Algorithmic identification is the crux for several binary analysis applications, including malware analysis, vulnerability discovery, and embedded firmware reverse engineering. However, data-driven and signature-based approaches often break down when encountering outlier realizations of a particular algorithm. Moreover, reverse engineering of domain-specific binaries often requires collaborative analysis between reverse engineers and domain experts. Communicating the behavior of an unidentified binary program to non-reverse engineers necessitates the recovery of algorithmic semantics in a human-digestible form. This paper presents PERFUME, a framework that extracts symbolic math expressions from low-level binary representations of an algorithm. PERFUME works by translating a symbolic output representation of a binary function to a high-level mathematical expression. In particular, we detail how source and target representations are generated for training a machine translation model. We integrate PERFUME as a plug-in for Ghidra--an open-source reverse engineering framework. We present our preliminary findings for domain-specific use cases and formalize open challenges in mathematical expression extraction from algorithmic implementations.},\nbooktitle = {Proceedings of the 2021 Research on Offensive and Defensive Techniques in the Context of Man At The End (MATE) Attacks},\npages = {59–69},\nnumpages = {11},\nkeywords = {reverse engineering, binary analysis},\nlocation = {Virtual Event, Republic of Korea},\nseries = {Checkmate '21}\n}\n\n\n\n\n
\n
\n\n\n
\n Algorithmic identification is the crux for several binary analysis applications, including malware analysis, vulnerability discovery, and embedded firmware reverse engineering. However, data-driven and signature-based approaches often break down when encountering outlier realizations of a particular algorithm. Moreover, reverse engineering of domain-specific binaries often requires collaborative analysis between reverse engineers and domain experts. Communicating the behavior of an unidentified binary program to non-reverse engineers necessitates the recovery of algorithmic semantics in a human-digestible form. This paper presents PERFUME, a framework that extracts symbolic math expressions from low-level binary representations of an algorithm. PERFUME works by translating a symbolic output representation of a binary function to a high-level mathematical expression. In particular, we detail how source and target representations are generated for training a machine translation model. We integrate PERFUME as a plug-in for Ghidra–an open-source reverse engineering framework. We present our preliminary findings for domain-specific use cases and formalize open challenges in mathematical expression extraction from algorithmic implementations.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification.\n \n \n \n \n\n\n \n Spangher, A., May, J., Shiang, S., & Deng, L.\n\n\n \n\n\n\n In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 498–517, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"MultitaskPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{spangher-etal-2021-multitask,\n    title = "Multitask Semi-Supervised Learning for Class-Imbalanced Discourse Classification",\n    author = "Spangher, Alexander  and\n      May, Jonathan  and\n      Shiang, Sz-Rung  and\n      Deng, Lingjia",\n    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",\n    month = nov,\n    year = "2021",\n    address = "Online and Punta Cana, Dominican Republic",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2021.emnlp-main.40",\n    pages = "498--517",\n    abstract = "As labeling schemas evolve over time, small differences can render datasets following older schemas unusable. This prevents researchers from building on top of previous annotation work and results in the existence, in discourse learning in particular, of many small class-imbalanced datasets. In this work, we show that a multitask learning approach can combine discourse datasets from similar and diverse domains to improve discourse classification. We show an improvement of 4.9{\\%} Micro F1-score over current state-of-the-art benchmarks on the \\textit{NewsDiscourse} dataset, one of the largest discourse datasets recently published, due in part to label correlations across tasks, which improve performance for underrepresented classes. We also offer an extensive review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in our setting.",\n}\n\n
\n
\n\n\n
\n As labeling schemas evolve over time, small differences can render datasets following older schemas unusable. This prevents researchers from building on top of previous annotation work and results in the existence, in discourse learning in particular, of many small class-imbalanced datasets. In this work, we show that a multitask learning approach can combine discourse datasets from similar and diverse domains to improve discourse classification. We show an improvement of 4.9% Micro F1-score over current state-of-the-art benchmarks on the NewsDiscourse dataset, one of the largest discourse datasets recently published, due in part to label correlations across tasks, which improve performance for underrepresented classes. We also offer an extensive review of additional techniques proposed to address resource-poor problems in NLP, and show that none of these approaches can improve classification accuracy in our setting.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Salience-Aware Event Chain Modeling for Narrative Understanding.\n \n \n \n \n\n\n \n Zhang, X., Chen, M., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1418–1428, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Salience-AwarePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{zhang-etal-2021-salience,\n    title = "Salience-Aware Event Chain Modeling for Narrative Understanding",\n    author = "Zhang, Xiyang  and\n      Chen, Muhao  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",\n    month = nov,\n    year = "2021",\n    address = "Online and Punta Cana, Dominican Republic",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2021.emnlp-main.107",\n    pages = "1418--1428",\n    abstract = "Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process. It is desirable to extract the event chains that represent such processes. However, this extraction remains a challenging problem. We posit that this is due to the nature of the texts from which chains are discovered. Natural language text interleaves a narrative of concrete, salient events with background information, contextualization, opinion, and other elements that are important for a variety of necessary discourse and pragmatics acts but are not part of the principal chain of events being communicated. We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences. We demonstrate the effectiveness of our methods at isolating critical event chains by comparing their effect on downstream tasks. We show that by pre-training large language models on our extracted chains, we obtain improvements in two tasks that benefit from a clear understanding of event chains: narrative prediction and event-based temporal question answering. The demonstrated improvements and ablative studies confirm that our extraction method isolates critical event chains.",\n}\n\n
\n
\n\n\n
\n Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a concrete process. It is desirable to extract the event chains that represent such processes. However, this extraction remains a challenging problem. We posit that this is due to the nature of the texts from which chains are discovered. Natural language text interleaves a narrative of concrete, salient events with background information, contextualization, opinion, and other elements that are important for a variety of necessary discourse and pragmatics acts but are not part of the principal chain of events being communicated. We introduce methods for extracting this principal chain from natural language text, by filtering away non-salient events and supportive sentences. We demonstrate the effectiveness of our methods at isolating critical event chains by comparing their effect on downstream tasks. We show that by pre-training large language models on our extracted chains, we obtain improvements in two tasks that benefit from a clear understanding of event chains: narrative prediction and event-based temporal question answering. The demonstrated improvements and ablative studies confirm that our extraction method isolates critical event chains.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation.\n \n \n \n \n\n\n \n Gheini, M., Ren, X., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1754–1765, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Cross-AttentionPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{gheini-etal-2021-cross,\n    title = "Cross-Attention is All You Need: {A}dapting Pretrained {T}ransformers for Machine Translation",\n    author = "Gheini, Mozhdeh  and\n      Ren, Xiang  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",\n    month = nov,\n    year = "2021",\n    address = "Online and Punta Cana, Dominican Republic",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2021.emnlp-main.132",\n    pages = "1754--1765",\n    abstract = "We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into cross-attention when training from scratch. We conduct a series of experiments through fine-tuning a translation model on data where either the source or target language has changed. These experiments reveal that fine-tuning only the cross-attention parameters is nearly as effective as fine-tuning all parameters (i.e., the entire translation model). We provide insights into why this is the case and observe that limiting fine-tuning in this manner yields cross-lingually aligned embeddings. The implications of this finding for researchers and practitioners include a mitigation of catastrophic forgetting, the potential for zero-shot translation, and the ability to extend machine translation models to several new language pairs with reduced parameter storage overhead.",\n}\n\n\n
\n
\n\n\n
\n We study the power of cross-attention in the Transformer architecture within the context of transfer learning for machine translation, and extend the findings of studies into cross-attention when training from scratch. We conduct a series of experiments through fine-tuning a translation model on data where either the source or target language has changed. These experiments reveal that fine-tuning only the cross-attention parameters is nearly as effective as fine-tuning all parameters (i.e., the entire translation model). We provide insights into why this is the case and observe that limiting fine-tuning in this manner yields cross-lingually aligned embeddings. The implications of this finding for researchers and practitioners include a mitigation of catastrophic forgetting, the potential for zero-shot translation, and the ability to extend machine translation models to several new language pairs with reduced parameter storage overhead.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Summary-Oriented Question Generation for Informational Queries.\n \n \n \n \n\n\n \n Yin, X., Zhou, L., Small, K., & May, J.\n\n\n \n\n\n\n In Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021), pages 81–97, Online, August 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Summary-OrientedPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{yin-etal-2021-summary,\n    title = "Summary-Oriented Question Generation for Informational Queries",\n    author = "Yin, Xusen  and\n      Zhou, Li  and\n      Small, Kevin  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 1st Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc 2021)",\n    month = aug,\n    year = "2021",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2021.dialdoc-1.11",\n    pages = "81--97",\n    abstract = "Users frequently ask simple factoid questions for question answering (QA) systems, attenuating the impact of myriad recent works that support more complex questions. Prompting users with automatically generated suggested questions (SQs) can improve user understanding of QA system capabilities and thus facilitate more effective use. We aim to produce self-explanatory questions that focus on main document topics and are answerable with variable length passages as appropriate. We satisfy these requirements by using a BERT-based Pointer-Generator Network trained on the Natural Questions (NQ) dataset. Our model shows SOTA performance of SQ generation on the NQ dataset (20.1 BLEU-4). We further apply our model on out-of-domain news articles, evaluating with a QA system due to the lack of gold questions and demonstrate that our model produces better SQs for news articles {--} with further confirmation via a human evaluation.",\n}\n\n\n\n
\n
\n\n\n
\n Users frequently ask simple factoid questions for question answering (QA) systems, attenuating the impact of myriad recent works that support more complex questions. Prompting users with automatically generated suggested questions (SQs) can improve user understanding of QA system capabilities and thus facilitate more effective use. We aim to produce self-explanatory questions that focus on main document topics and are answerable with variable length passages as appropriate. We satisfy these requirements by using a BERT-based Pointer-Generator Network trained on the Natural Questions (NQ) dataset. Our model shows SOTA performance of SQ generation on the NQ dataset (20.1 BLEU-4). We further apply our model on out-of-domain news articles, evaluating with a QA system due to the lack of gold questions and demonstrate that our model produces better SQs for news articles – with further confirmation via a human evaluation.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n On the Strengths of Cross-Attention in Pretrained Transformers for Machine Translation.\n \n \n \n\n\n \n Gheini, M., Ren, X., & May, J.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{gheini2021strengths,\n      title={On the Strengths of Cross-Attention in Pretrained Transformers for Machine Translation}, \n      author={Mozhdeh Gheini and Xiang Ren and Jonathan May},\n      year={2021},\n      eprint={2104.08771},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Many-to-English Machine Translation Tools, Data, and Pretrained Models.\n \n \n \n \n\n\n \n Gowda, T., Zhang, Z., Mattmann, C., & May, J.\n\n\n \n\n\n\n In Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 306–316, Online, August 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Many-to-EnglishPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{gowda-etal-2021-many,\n    title = "Many-to-{E}nglish Machine Translation Tools, Data, and Pretrained Models",\n    author = "Gowda, Thamme  and\n      Zhang, Zhao  and\n      Mattmann, Chris  and\n      May, Jonathan",\n    booktitle = "Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",\n    month = aug,\n    year = "2021",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2021.acl-demo.37",\n    pages = "306--316",\n    abstract = "While there are more than 7000 languages in the world, most translation research efforts have targeted a few high resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.",\n}\n\n\n
\n
\n\n\n
\n While there are more than 7000 languages in the world, most translation research efforts have targeted a few high resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n WARP: Word-level Adversarial ReProgramming.\n \n \n \n \n\n\n \n Hambardzumyan, K., Khachatrian, H., & May, J.\n\n\n \n\n\n\n In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4921–4933, Online, August 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"WARP:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{hambardzumyan-etal-2021-warp,\n    title = "{WARP}: {W}ord-level {A}dversarial {R}e{P}rogramming",\n    author = "Hambardzumyan, Karen  and\n      Khachatrian, Hrant  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",\n    month = aug,\n    year = "2021",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2021.acl-long.381",\n    pages = "4921--4933",\n    abstract = "Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model. In this paper, we present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation. Adversarial reprogramming attempts to learn task-specific word embeddings that, when concatenated to the input text, instruct the language model to solve the specified task. Using up to 25K trainable parameters per task, this approach outperforms all existing methods with up to 25M trainable parameters on the public leaderboard of the GLUE benchmark. Our method, initialized with task-specific human-readable prompts, also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks with just 32 training samples.",\n}\n\n
\n
\n\n\n
\n Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model. In this paper, we present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation. Adversarial reprogramming attempts to learn task-specific word embeddings that, when concatenated to the input text, instruct the language model to solve the specified task. Using up to 25K trainable parameters per task, this approach outperforms all existing methods with up to 25M trainable parameters on the public leaderboard of the GLUE benchmark. Our method, initialized with task-specific human-readable prompts, also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks with just 32 training samples.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Can Sequence-to-Sequence Models Crack Substitution Ciphers?.\n \n \n \n \n\n\n \n Aldarrab, N., & May, J.\n\n\n \n\n\n\n In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7226–7235, Online, August 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"CanPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{aldarrab-may-2021-sequence,\n    title = "Can Sequence-to-Sequence Models Crack Substitution Ciphers?",\n    author = "Aldarrab, Nada  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",\n    month = aug,\n    year = "2021",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2021.acl-long.561",\n    pages = "7226--7235",\n    abstract = "Decipherment of historical ciphers is a challenging problem. The language of the target plaintext might be unknown, and ciphertext can have a lot of noise. State-of-the-art decipherment methods use beam search and a neural language model to score candidate plaintext hypotheses for a given cipher, assuming the plaintext language is known. We propose an end-to-end multilingual model for solving simple substitution ciphers. We test our model on synthetic and real historical ciphers and show that our proposed method can decipher text without explicit language identification while still being robust to noise.",\n}\n\n
\n
\n\n\n
\n Decipherment of historical ciphers is a challenging problem. The language of the target plaintext might be unknown, and ciphertext can have a lot of noise. State-of-the-art decipherment methods use beam search and a neural language model to score candidate plaintext hypotheses for a given cipher, assuming the plaintext language is known. We propose an end-to-end multilingual model for solving simple substitution ciphers. We test our model on synthetic and real historical ciphers and show that our proposed method can decipher text without explicit language identification while still being robust to noise.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering.\n \n \n \n \n\n\n \n M'hamdi, M., Kim, D. S., Dernoncourt, F., Bui, T., Ren, X., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3617–3632, Online, June 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"X-METRA-ADA:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{mhamdi-etal-2021-x,\n    title = "{X}-{METRA}-{ADA}: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering",\n    author = "M{'}hamdi, Meryem  and\n      Kim, Doo Soon  and\n      Dernoncourt, Franck  and\n      Bui, Trung  and\n      Ren, Xiang  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",\n    month = jun,\n    year = "2021",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2021.naacl-main.283",\n    pages = "3617--3632",\n    abstract = "Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization ability is still inconsistent for typologically diverse languages and across different benchmarks. Recently, meta-learning has garnered attention as a promising technique for enhancing transfer learning under low-resource scenarios: particularly for cross-lingual transfer in Natural Language Understanding (NLU). In this work, we propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for NLU. Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages. We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering. We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages. Our analysis reveals that X-METRA-ADA can leverage limited data for faster adaptation.",\n}\n\n\n
\n
\n\n\n
\n Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities. However, their generalization ability is still inconsistent for typologically diverse languages and across different benchmarks. Recently, meta-learning has garnered attention as a promising technique for enhancing transfer learning under low-resource scenarios: particularly for cross-lingual transfer in Natural Language Understanding (NLU). In this work, we propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for NLU. Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages. We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering. We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages. Our analysis reveals that X-METRA-ADA can leverage limited data for faster adaptation.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n CaSiNo: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems.\n \n \n \n \n\n\n \n Chawla, K., Ramirez, J., Clever, R., Lucas, G., May, J., & Gratch, J.\n\n\n \n\n\n\n In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3167–3185, Online, June 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"CaSiNo:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chawla-etal-2021-casino,\n    title = "{C}a{S}i{N}o: A Corpus of Campsite Negotiation Dialogues for Automatic Negotiation Systems",\n    author = "Chawla, Kushal  and\n      Ramirez, Jaysa  and\n      Clever, Rene  and\n      Lucas, Gale  and\n      May, Jonathan  and\n      Gratch, Jonathan",\n    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",\n    month = jun,\n    year = "2021",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2021.naacl-main.254",\n    pages = "3167--3185",\n    abstract = "Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for food, water, and firewood packages for their upcoming trip. Our design results in diverse and linguistically rich negotiations while maintaining a tractable, closed-domain environment. Inspired by the literature in human-human negotiations, we annotate persuasion strategies and perform correlation analysis to understand how the dialogue behaviors are associated with the negotiation performance. We further propose and evaluate a multi-task framework to recognize these strategies in a given utterance. We find that multi-task learning substantially improves the performance for all strategy labels, especially for the ones that are the most skewed. We release the dataset, annotations, and the code to propel future work in human-machine negotiations: https://github.com/kushalchawla/CaSiNo",\n}\n\n
\n
\n\n\n
\n Automated systems that negotiate with humans have broad applications in pedagogy and conversational AI. To advance the development of practical negotiation systems, we present CaSiNo: a novel corpus of over a thousand negotiation dialogues in English. Participants take the role of campsite neighbors and negotiate for food, water, and firewood packages for their upcoming trip. Our design results in diverse and linguistically rich negotiations while maintaining a tractable, closed-domain environment. Inspired by the literature in human-human negotiations, we annotate persuasion strategies and perform correlation analysis to understand how the dialogue behaviors are associated with the negotiation performance. We further propose and evaluate a multi-task framework to recognize these strategies in a given utterance. We find that multi-task learning substantially improves the performance for all strategy labels, especially for the ones that are the most skewed. We release the dataset, annotations, and the code to propel future work in human-machine negotiations: https://github.com/kushalchawla/CaSiNo\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Macro-Average: Rare Types Are Important Too.\n \n \n \n \n\n\n \n Gowda, T., You, W., Lignos, C., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1138–1157, Online, June 2021. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Macro-Average:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{gowda-etal-2021-macro,\n    title = "Macro-Average: Rare Types Are Important Too",\n    author = "Gowda, Thamme  and\n      You, Weiqiu  and\n      Lignos, Constantine  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",\n    month = jun,\n    year = "2021",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2021.naacl-main.90",\n    pages = "1138--1157",\n    abstract = "While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require potentially expensive re-training for new domains and languages. Furthermore, their decisions are inherently non-transparent and appear to reflect unwelcome biases. We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. We find that MacroF1 is competitive on direct assessment, and outperforms others in indicating downstream cross-lingual information retrieval task performance. Further, we show that MacroF1 can be used to effectively compare supervised and unsupervised neural machine translation, and reveal significant qualitative differences in the methods{'} outputs.",\n}\n\n\n
\n
\n\n\n
\n While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require potentially expensive re-training for new domains and languages. Furthermore, their decisions are inherently non-transparent and appear to reflect unwelcome biases. We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. We find that MacroF1 is competitive on direct assessment, and outperforms others in indicating downstream cross-lingual information retrieval task performance. Further, we show that MacroF1 can be used to effectively compare supervised and unsupervised neural machine translation, and reveal significant qualitative differences in the methods' outputs.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n WARP: Word-level Adversarial ReProgramming.\n \n \n \n\n\n \n Hambardzumyan, K., Khachatrian, H., & May, J.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{hambardzumyan2021warp,\n      title={WARP: Word-level Adversarial ReProgramming}, \n      author={Karen Hambardzumyan and Hrant Khachatrian and Jonathan May},\n      year={2021},\n      eprint={2101.00121},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Multitask Learning for Class-Imbalanced Discourse Classification.\n \n \n \n\n\n \n Spangher, A., May, J., Shiang, S., & Deng, L.\n\n\n \n\n\n\n 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{spangher2021multitask,\n      title={Multitask Learning for Class-Imbalanced Discourse Classification}, \n      author={Alexander Spangher and Jonathan May and Sz-rung Shiang and Lingjia Deng},\n      year={2021},\n      eprint={2101.00389},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2020\n \n \n (25)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Apollo: An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization.\n \n \n \n\n\n \n Ma, X.\n\n\n \n\n\n\n arXiv preprint arXiv:2009.13586. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{ma2020apollo,\n  title={Apollo: An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization},\n  author={Ma, Xuezhe},\n  journal={arXiv preprint arXiv:2009.13586},\n  year={2020}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Joint Constrained Learning for Event-Event Relation Extraction.\n \n \n \n\n\n \n Wang, H., Chen, M., Zhang, H., & Roth, D.\n\n\n \n\n\n\n In EMNLP, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{wang2020joint,\n  title={Joint Constrained Learning for Event-Event Relation Extraction},\n  author={Wang, Haoyu and Chen, Muhao and Zhang, Hongming and Roth, Dan},\n  booktitle={EMNLP},\n  year={2020}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Analogous Process Structure Induction for Sub-event Sequence Prediction.\n \n \n \n\n\n \n Zhang, H., Chen, M., Wang, H., Song, Y., & Roth, D.\n\n\n \n\n\n\n In EMNLP, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{zhang2020analogous,\n  title={Analogous Process Structure Induction for Sub-event Sequence Prediction},\n  author={Zhang, Hongming and Chen, Muhao and Wang, Haoyu and Song, Yangqiu and Roth, Dan},\n  booktitle={EMNLP},\n  year={2020}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Knowledge Association with Hyperbolic Knowledge Graph Embeddings.\n \n \n \n\n\n \n Sun, Z., Chen, M., Hu, W., Wang, C., Dai, J., & Zhang, W.\n\n\n \n\n\n\n In EMNLP, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{sun2020knowledge,\n  title={Knowledge Association with Hyperbolic Knowledge Graph Embeddings},\n  author={Sun, Zequn and Chen, Muhao and Hu, Wei and Wang, Chengming and Dai, Jian and Zhang, Wei},\n  booktitle={EMNLP},\n  year={2020}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer.\n \n \n \n\n\n \n Chen, X., Chen, M., Fan, C., Uppunda, A., Sun, Y., & Zaniolo, C.\n\n\n \n\n\n\n In EMNLP - Findings, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chen2020multilingual,\n  title={Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer},\n  author={Chen, Xuelu and Chen, Muhao and Fan, Changjun and Uppunda, Ankith and Sun, Yizhou and Zaniolo, Carlo},\n  booktitle={EMNLP - Findings},\n  year={2020}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n What Are You Trying to Do? Semantic Typing of Event Processes.\n \n \n \n\n\n \n Chen, M., Zhang, H., Wang, H., & Roth, D.\n\n\n \n\n\n\n In CoNLL, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{chen2020you,\n  title={What Are You Trying to Do? Semantic Typing of Event Processes},\n  author={Chen, Muhao and Zhang, Hongming and Wang, Haoyu and Roth, Dan},\n  booktitle={CoNLL},\n  year={2020}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Bio-joie: Joint representation learning of biological knowledge bases.\n \n \n \n\n\n \n Hao, J., Ju, C. J., Chen, M., Sun, Y., Zaniolo, C., & Wang, W.\n\n\n \n\n\n\n In ACM BCB, pages 1–10, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{hao2020bio,\n  title={Bio-joie: Joint representation learning of biological knowledge bases},\n  author={Hao, Junheng and Ju, Chelsea J-T and Chen, Muhao and Sun, Yizhou and Zaniolo, Carlo and Wang, Wei},\n  booktitle={ACM BCB},\n  pages={1--10},\n  year={2020}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n A benchmarking study of embedding-based entity alignment for knowledge graphs.\n \n \n \n\n\n \n Sun, Z., Zhang, Q., Hu, W., Wang, C., Chen, M., Akrami, F., & Li, C.\n\n\n \n\n\n\n Proceedings of the VLDB Endowment, 13(12). 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{sun2020benchmarking,\n  title={A benchmarking study of embedding-based entity alignment for knowledge graphs},\n  author={Sun, Zequn and Zhang, Qingheng and Hu, Wei and Wang, Chengming and Chen, Muhao and Akrami, Farahnaz and Li, Chengkai},\n  journal={Proceedings of the VLDB Endowment},\n  volume={13},\n  number={12},\n  year={2020}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation.\n \n \n \n\n\n \n Sun, Z., Wang, C., Hu, W., Chen, M., Dai, J., Zhang, W., & Qu, Y.\n\n\n \n\n\n\n In AAAI, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{sun2020knowledge,\n  title={Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation},\n  author={Sun, Zequn and Wang, Chengming and Hu, Wei and Chen, Muhao and Dai, Jian and Zhang, Wei and Qu, Yuzhong},\n  booktitle={AAAI},\n  year={2020}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Recent advances in transferable representation learning.\n \n \n \n\n\n \n Chen, M., Chang, K., & Roth, D.\n\n\n \n\n\n\n AAAI Tutorials. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{chen2020recent,\n  title={Recent advances in transferable representation learning},\n  author={Chen, Muhao and Chang, Kai-Wei and Roth, Dan},\n  journal={AAAI Tutorials},\n  year={2020}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Mutation effect estimation on protein–protein interactions using deep contextualized representation learning.\n \n \n \n\n\n \n Zhou, G., Chen, M., Ju, C. J., Wang, Z., Jiang, J., & Wang, W.\n\n\n \n\n\n\n NAR genomics and bioinformatics, 2(2): lqaa015. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{zhou2020mutation,\n  title={Mutation effect estimation on protein--protein interactions using deep contextualized representation learning},\n  author={Zhou, Guangyu and Chen, Muhao and Ju, Chelsea JT and Wang, Zheng and Jiang, Jyun-Yu and Wang, Wei},\n  journal={NAR genomics and bioinformatics},\n  volume={2},\n  number={2},\n  pages={lqaa015},\n  year={2020},\n  publisher={Oxford University Press}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Readnet: A hierarchical transformer framework for web article readability analysis.\n \n \n \n\n\n \n Meng, C., Chen, M., Mao, J., & Neville, J.\n\n\n \n\n\n\n In European Conference on Information Retrieval, pages 33–49, 2020. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{meng2020readnet,\n  title={Readnet: A hierarchical transformer framework for web article readability analysis},\n  author={Meng, Changping and Chen, Muhao and Mao, Jie and Neville, Jennifer},\n  booktitle={European Conference on Information Retrieval},\n  pages={33--49},\n  year={2020},\n  organization={Springer}\n}\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Diagnostic prediction with sequence-of-sets representation learning for clinical events.\n \n \n \n\n\n \n Zhang, T., Chen, M., & Bui, A. A.\n\n\n \n\n\n\n In International Conference on Artificial Intelligence in Medicine, pages 348–358, 2020. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{zhang2020diagnostic,\n  title={Diagnostic prediction with sequence-of-sets representation learning for clinical events},\n  author={Zhang, Tianran and Chen, Muhao and Bui, Alex AT},\n  booktitle={International Conference on Artificial Intelligence in Medicine},\n  pages={348--358},\n  year={2020},\n  organization={Springer}\n}\n\n\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Question Generation for Supporting Informational Query Intents.\n \n \n \n\n\n \n Yin, X., May, J., Zhou, L., & Small, K.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{yin2020question,\n      title={Question Generation for Supporting Informational Query Intents}, \n      author={Xusen Yin and Jonathan May and Li Zhou and Kevin Small},\n      year={2020},\n      eprint={2010.09692},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Can Sequence-to-Sequence Models Crack Substitution Ciphers?.\n \n \n \n\n\n \n Aldarrab, N., & May, J.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@misc{aldarrab2020sequencetosequence,\n      title={Can Sequence-to-Sequence Models Crack Substitution Ciphers?}, \n      author={Nada Aldarrab and Jonathan May},\n      year={2020},\n      eprint={2012.15229},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Proceedings of the Fourteenth Workshop on Semantic Evaluation.\n \n \n \n \n\n\n \n Herbelot, A., Zhu, X., Palmer, A., Schneider, N., May, J., & Shutova, E.,\n editors.\n \n\n\n \n\n\n\n International Committee for Computational Linguistics. Barcelona (online), December 2020.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@proceedings{semeval-2020-semantic,\n    title = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",\n    editor = "Herbelot, Aurelie  and\n      Zhu, Xiaodan  and\n      Palmer, Alexis  and\n      Schneider, Nathan  and\n      May, Jonathan  and\n      Shutova, Ekaterina",\n    month = dec,\n    year = "2020",\n    address = "Barcelona (online)",\n    publisher = "International Committee for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2020.semeval-1.0",\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020.\n \n \n \n \n\n\n \n Verspoor, K., Cohen, K. B., Dredze, M., Ferrara, E., May, J., Munro, R., Paris, C., & Wallace, B.,\n editors.\n \n\n\n \n\n\n\n Association for Computational Linguistics. Online, July 2020.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@proceedings{nlp-covid19-2020-nlp,\n    title = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",\n    editor = "Verspoor, Karin  and\n      Cohen, Kevin Bretonnel  and\n      Dredze, Mark  and\n      Ferrara, Emilio  and\n      May, Jonathan  and\n      Munro, Robert  and\n      Paris, Cecile  and\n      Wallace, Byron",\n    month = jul,\n    year = "2020",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.0",\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Enabling Low-Resource Transfer Learning across COVID-19 Corpora by Combining Event-Extraction and Co-Training.\n \n \n \n \n\n\n \n Spangher, A., Peng, N., May, J., & Ferrara, E.\n\n\n \n\n\n\n In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online, July 2020. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"EnablingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{spangher-etal-2020-enabling,\n    title = "Enabling Low-Resource Transfer Learning across {COVID-19} Corpora by Combining Event-Extraction and Co-Training",\n    author = "Spangher, Alexander  and\n      Peng, Nanyun  and\n      May, Jonathan  and\n      Ferrara, Emilio",\n    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",\n    month = jul,\n    year = "2020",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2020.nlpcovid19-acl.4",\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Learning to Generalize for Sequential Decision Making.\n \n \n \n \n\n\n \n Yin, X., Weischedel, R., & May, J.\n\n\n \n\n\n\n In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3046–3063, Online, November 2020. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"LearningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{yin-etal-2020-learning,\n    title = "Learning to Generalize for Sequential Decision Making",\n    author = "Yin, Xusen  and\n      Weischedel, Ralph  and\n      May, Jonathan",\n    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",\n    month = nov,\n    year = "2020",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.273",\n    pages = "3046--3063",\n    abstract = "We consider problems of making sequences of decisions to accomplish tasks, interacting via the medium of language. These problems are often tackled with reinforcement learning approaches. We find that these models do not generalize well when applied to novel task domains. However, the large amount of computation necessary to adequately train and explore the search space of sequential decision making, under a reinforcement learning paradigm, precludes the inclusion of large contextualized language models, which might otherwise enable the desired generalization ability. We introduce a teacher-student imitation learning methodology and a means of converting a reinforcement learning model into a natural language understanding model. Together, these methodologies enable the introduction of contextualized language models into the sequential decision making problem space. We show that models can learn faster and generalize more, leveraging both the imitation learning and the reformulation. Our models exceed teacher performance on various held-out decision problems, by up to 7{\\%} on in-domain problems and 24{\\%} on out-of-domain problems.",\n}\n\n
\n
\n\n\n
\n We consider problems of making sequences of decisions to accomplish tasks, interacting via the medium of language. These problems are often tackled with reinforcement learning approaches. We find that these models do not generalize well when applied to novel task domains. However, the large amount of computation necessary to adequately train and explore the search space of sequential decision making, under a reinforcement learning paradigm, precludes the inclusion of large contextualized language models, which might otherwise enable the desired generalization ability. We introduce a teacher-student imitation learning methodology and a means of converting a reinforcement learning model into a natural language understanding model. Together, these methodologies enable the introduction of contextualized language models into the sequential decision making problem space. We show that models can learn faster and generalize more, leveraging both the imitation learning and the reformulation. Our models exceed teacher performance on various held-out decision problems, by up to 7% on in-domain problems and 24% on out-of-domain problems.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Finding the Optimal Vocabulary Size for Neural Machine Translation.\n \n \n \n \n\n\n \n Gowda, T., & May, J.\n\n\n \n\n\n\n In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3955–3964, Online, November 2020. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"FindingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{gowda-may-2020-finding,\n    title = "Finding the Optimal Vocabulary Size for Neural Machine Translation",\n    author = "Gowda, Thamme  and\n      May, Jonathan",\n    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",\n    month = nov,\n    year = "2020",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.352",\n    pages = "3955--3964",\n    abstract = "We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components. Classifiers are known to perform better with balanced class distributions during training. Since the Zipfian nature of languages causes imbalanced classes, we explore its effect on NMT. We analyze the effect of various vocabulary sizes on NMT performance on multiple languages with many data sizes, and reveal an explanation for why certain vocabulary sizes are better than others.",\n}\n\n
\n
\n\n\n
\n We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components. Classifiers are known to perform better with balanced class distributions during training. Since the Zipfian nature of languages causes imbalanced classes, we explore its effect on NMT. We analyze the effect of various vocabulary sizes on NMT performance on multiple languages with many data sizes, and reveal an explanation for why certain vocabulary sizes are better than others.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Connecting the Dots: Event Graph Schema Induction with Path Language Modeling.\n \n \n \n \n\n\n \n Li, M., Zeng, Q., Lin, Y., Cho, K., Ji, H., May, J., Chambers, N., & Voss, C.\n\n\n \n\n\n\n In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 684–695, Online, November 2020. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ConnectingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{li-etal-2020-connecting,\n    title = "Connecting the Dots: Event Graph Schema Induction with Path Language Modeling",\n    author = "Li, Manling  and\n      Zeng, Qi  and\n      Lin, Ying  and\n      Cho, Kyunghyun  and\n      Ji, Heng  and\n      May, Jonathan  and\n      Chambers, Nathanael  and\n      Voss, Clare",\n    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n    month = nov,\n    year = "2020",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2020.emnlp-main.50",\n    pages = "684--695",\n    abstract = "Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. We propose a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story. We then introduce Path Language Model, an auto-regressive language model trained on event-event paths, and select salient and coherent paths to probabilistically construct these graph schemas. We design two evaluation metrics, instance coverage and instance coherence, to evaluate the quality of graph schema induction, by checking when coherent event instances are covered by the schema graph. Intrinsic evaluations show that our approach is highly effective at inducing salient and coherent schemas. Extrinsic evaluations show the induced schema repository provides significant improvement to downstream end-to-end Information Extraction over a state-of-the-art joint neural extraction model, when used as additional global features to unfold instance graphs.",\n}\n\n
\n
\n\n\n
\n Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. We propose a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story. We then introduce Path Language Model, an auto-regressive language model trained on event-event paths, and select salient and coherent paths to probabilistically construct these graph schemas. We design two evaluation metrics, instance coverage and instance coherence, to evaluate the quality of graph schema induction, by checking when coherent event instances are covered by the schema graph. Intrinsic evaluations show that our approach is highly effective at inducing salient and coherent schemas. Extrinsic evaluations show the induced schema repository provides significant improvement to downstream end-to-end Information Extraction over a state-of-the-art joint neural extraction model, when used as additional global features to unfold instance graphs.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Experience Grounds Language.\n \n \n \n \n\n\n \n Bisk, Y., Holtzman, A., Thomason, J., Andreas, J., Bengio, Y., Chai, J., Lapata, M., Lazaridou, A., May, J., Nisnevich, A., Pinto, N., & Turian, J.\n\n\n \n\n\n\n In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8718–8735, Online, November 2020. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ExperiencePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{bisk-etal-2020-experience,\n    title = "Experience Grounds Language",\n    author = "Bisk, Yonatan  and\n      Holtzman, Ari  and\n      Thomason, Jesse  and\n      Andreas, Jacob  and\n      Bengio, Yoshua  and\n      Chai, Joyce  and\n      Lapata, Mirella  and\n      Lazaridou, Angeliki  and\n      May, Jonathan  and\n      Nisnevich, Aleksandr  and\n      Pinto, Nicolas  and\n      Turian, Joseph",\n    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n    month = nov,\n    year = "2020",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2020.emnlp-main.703",\n    pages = "8718--8735",\n    abstract = "Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.",\n}\n\n
\n
\n\n\n
\n Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Cross-lingual Structure Transfer for Zero-resource Event Extraction.\n \n \n \n \n\n\n \n Lu, D., Subburathinam, A., Ji, H., May, J., Chang, S., Sil, A., & Voss, C.\n\n\n \n\n\n\n In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1976–1981, Marseille, France, May 2020. European Language Resources Association\n \n\n\n\n
\n\n\n\n \n \n \"Cross-lingualPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{lu-etal-2020-cross,\n    title = "Cross-lingual Structure Transfer for Zero-resource Event Extraction",\n    author = "Lu, Di  and\n      Subburathinam, Ananya  and\n      Ji, Heng  and\n      May, Jonathan  and\n      Chang, Shih-Fu  and\n      Sil, Avi  and\n      Voss, Clare",\n    booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",\n    month = may,\n    year = "2020",\n    address = "Marseille, France",\n    publisher = "European Language Resources Association",\n    url = "https://www.aclweb.org/anthology/2020.lrec-1.243",\n    pages = "1976--1981",\n    abstract = "Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging. To tackle more complex tasks such as event extraction we need to transfer graph structures (event trigger linked to multiple arguments with various roles) across languages. We develop a novel share-and-transfer framework to reach this goal with three steps: (1) Convert each sentence in any language to language-universal graph structures; in this paper we explore two approaches based on universal dependency parses and complete graphs, respectively. (2) Represent each node in the graph structure with a cross-lingual word embedding so that all sentences in multiple languages can be represented with one shared semantic space. (3) Using this common semantic space, train event extractors from English training data and apply them to languages that do not have any event annotations. Experimental results on three languages (Spanish, Russian and Ukrainian) without any annotations show this framework achieves comparable performance to a state-of-the-art supervised model trained from more than 1,500 manually annotated event mentions.",\n    language = "English",\n    ISBN = "979-10-95546-34-4",\n}\n\n
\n
\n\n\n
\n Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging. To tackle more complex tasks such as event extraction we need to transfer graph structures (event trigger linked to multiple arguments with various roles) across languages. We develop a novel share-and-transfer framework to reach this goal with three steps: (1) Convert each sentence in any language to language-universal graph structures; in this paper we explore two approaches based on universal dependency parses and complete graphs, respectively. (2) Represent each node in the graph structure with a cross-lingual word embedding so that all sentences in multiple languages can be represented with one shared semantic space. (3) Using this common semantic space, train event extractors from English training data and apply them to languages that do not have any event annotations. Experimental results on three languages (Spanish, Russian and Ukrainian) without any annotations show this framework achieves comparable performance to a state-of-the-art supervised model trained from more than 1,500 manually annotated event mentions.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Grounding Conversations with Improvised Dialogues.\n \n \n \n \n\n\n \n Cho, H., & May, J.\n\n\n \n\n\n\n In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2398–2413, Online, July 2020. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"GroundingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{cho-may-2020-grounding,\n    title = "Grounding Conversations with Improvised Dialogues",\n    author = "Cho, Hyundong  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",\n    month = jul,\n    year = "2020",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/2020.acl-main.218",\n    doi = "10.18653/v1/2020.acl-main.218",\n    pages = "2398--2413",\n    abstract = "Effective dialogue involves grounding, the process of establishing mutual knowledge that is essential for communication between people. Modern dialogue systems are not explicitly trained to build common ground, and therefore overlook this important aspect of communication. Improvisational theater (improv) intrinsically contains a high proportion of dialogue focused on building common ground, and makes use of the yes-and principle, a strong grounding speech act, to establish coherence and an actionable objective reality. We collect a corpus of more than 26,000 yes-and turns, transcribing them from improv dialogues and extracting them from larger, but more sparsely populated movie script dialogue corpora, via a bootstrapped classifier. We fine-tune chit-chat dialogue systems with our corpus to encourage more grounded, relevant conversation and confirm these findings with human evaluations.",\n}\n\n\n
\n
\n\n\n
\n Effective dialogue involves grounding, the process of establishing mutual knowledge that is essential for communication between people. Modern dialogue systems are not explicitly trained to build common ground, and therefore overlook this important aspect of communication. Improvisational theater (improv) intrinsically contains a high proportion of dialogue focused on building common ground, and makes use of the yes-and principle, a strong grounding speech act, to establish coherence and an actionable objective reality. We collect a corpus of more than 26,000 yes-and turns, transcribing them from improv dialogues and extracting them from larger, but more sparsely populated movie script dialogue corpora, via a bootstrapped classifier. We fine-tune chit-chat dialogue systems with our corpus to encourage more grounded, relevant conversation and confirm these findings with human evaluations.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n “Don’t quote me on that”: Finding Mixtures of Sources in News Articles.\n \n \n \n\n\n \n Alexander Spangher, N. P., & Ferrara, E.\n\n\n \n\n\n\n In Proc. Computation+Journalism Symposium, Online, March 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{spangher-2020-quote,\n  author =       {Alexander Spangher, Nanyun Peng, Jonathan May, and Emilio Ferrara},\n  title =        {“Don’t quote me on that”: Finding Mixtures of Sources in\nNews Articles},\n  booktitle = {Proc. Computation+Journalism Symposium},\n  year =      2020,\n  month =     {March},\n  address =   {Online}}\n\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2019\n \n \n (13)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n A Universal Parent Model for Low-Resource Neural Machine Translation Transfer.\n \n \n \n \n\n\n \n Gheini, M., & May, J.\n\n\n \n\n\n\n CoRR, abs/1909.06516. 2019.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/abs-1909-06516,\n  author    = {Mozhdeh Gheini and\n               Jonathan May},\n  title     = {A Universal Parent Model for Low-Resource Neural Machine Translation\n               Transfer},\n  journal   = {CoRR},\n  volume    = {abs/1909.06516},\n  year      = {2019},\n  url       = {http://arxiv.org/abs/1909.06516},\n  archivePrefix = {arXiv},\n  eprint    = {1909.06516},\n  timestamp = {Mon, 23 Sep 2019 18:07:15 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/abs-1909-06516.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n III.3: Evidence and Artificial Intelligence.\n \n \n \n\n\n \n Dane, J. A., & May, J.\n\n\n \n\n\n\n of Mythodologies. Begging The Question: Critical Reasoning in Chaucer Studies, Book History, and Humanistic Inquiry, pages 295–208. Marymount Institute Press, 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InBook{mythodologies-2019,\n  author =    {Joseph A. Dane and Jonathan May},\n  title =        {Begging The Question: Critical Reasoning in Chaucer Studies, Book History, and Humanistic Inquiry},\n  chapter =      {III.3: Evidence and Artificial Intelligence},\n  publisher =    {Marymount Institute Press},\n  year =         2019,\n  number =    {II},\n  series =    {Mythodologies},\n  pages =     {295--208}}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining.\n \n \n \n \n\n\n \n Pan, X., Gowda, T., Ji, H., May, J., & Miller, S.\n\n\n \n\n\n\n In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 56–66, Hong Kong, China, November 2019. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Cross-lingualPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{pan-etal-2019-cross,\n    title = "Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining",\n    author = "Pan, Xiaoman  and\n      Gowda, Thamme  and\n      Ji, Heng  and\n      May, Jonathan  and\n      Miller, Scott",\n    booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",\n    month = nov,\n    year = "2019",\n    address = "Hong Kong, China",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/D19-6107",\n    doi = "10.18653/v1/D19-6107",\n    pages = "56--66",\n    abstract = "Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1{\\%} absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.",\n}\n\n
\n
\n\n\n
\n Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1% absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources.\n \n \n \n \n\n\n \n M'hamdi, M., Freedman, M., & May, J.\n\n\n \n\n\n\n In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 656–665, Hong Kong, China, November 2019. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ContextualizedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{mhamdi-etal-2019-contextualized,\n    title = "Contextualized Cross-Lingual Event Trigger Extraction with Minimal Resources",\n    author = "M{'}hamdi, Meryem  and\n      Freedman, Marjorie  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",\n    month = nov,\n    year = "2019",\n    address = "Hong Kong, China",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/K19-1061",\n    doi = "10.18653/v1/K19-1061",\n    pages = "656--665",\n    abstract = "Event trigger extraction is an information extraction task of practical utility, yet it is challenging due to the difficulty of disambiguating word sense meaning. Previous approaches rely extensively on hand-crafted language-specific features and are applied mainly to English for which annotated datasets and Natural Language Processing (NLP) tools are available. However, the availability of such resources varies from one language to another. Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. However, there has not been much effort in exploring language transfer using BERT for event extraction. In this work, we treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features. We experiment with different flavors of transfer learning from high-resourced to low-resourced languages and compare the performance of different multilingual embeddings for event trigger extraction. Our results show that training in a multilingual setting outperforms language-specific models for both English and Chinese. Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.",\n}\n\n
\n
\n\n\n
\n Event trigger extraction is an information extraction task of practical utility, yet it is challenging due to the difficulty of disambiguating word sense meaning. Previous approaches rely extensively on hand-crafted language-specific features and are applied mainly to English for which annotated datasets and Natural Language Processing (NLP) tools are available. However, the availability of such resources varies from one language to another. Recently, contextualized Bidirectional Encoder Representations from Transformers (BERT) models have established state-of-the-art performance for a variety of NLP tasks. However, there has not been much effort in exploring language transfer using BERT for event extraction. In this work, we treat event trigger extraction as a sequence tagging problem and propose a cross-lingual framework for training it without any hand-crafted features. We experiment with different flavors of transfer learning from high-resourced to low-resourced languages and compare the performance of different multilingual embeddings for event trigger extraction. Our results show that training in a multilingual setting outperforms language-specific models for both English and Chinese. Our work is the first to experiment with two event architecture variants in a cross-lingual setting, to show the effectiveness of contextualized embeddings obtained using BERT, and to explore and analyze its performance on Arabic.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects.\n \n \n \n \n\n\n \n Mullenbach, J., Gordon, J., Peng, N., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6051–6057, Hong Kong, China, November 2019. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"DoPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{mullenbach-etal-2019-nuclear,\n    title = "Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects",\n    author = "Mullenbach, James  and\n      Gordon, Jonathan  and\n      Peng, Nanyun  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",\n    month = nov,\n    year = "2019",\n    address = "Hong Kong, China",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/D19-1625",\n    doi = "10.18653/v1/D19-1625",\n    pages = "6051--6057",\n    abstract = "How do adjectives project from a noun to its parts? If a motorcycle is red, are its wheels red? Is a nuclear submarine{'}s captain nuclear? These questions are easy for humans to judge using our commonsense understanding of the world, but are difficult for computers. To attack this challenge, we crowdsource a set of human judgments that answer the English-language question {``}Given a whole described by an adjective, does the adjective also describe a given part?{''} We build strong baselines for this task with a classification approach. Our findings indicate that, despite the recent successes of large language models on tasks aimed to assess commonsense knowledge, these models do not greatly outperform simple word-level models based on pre-trained word embeddings. This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings. Our dataset will serve as a useful testbed for future research in commonsense reasoning, especially as it relates to adjectives and objects",\n}\n\n
\n
\n\n\n
\n How do adjectives project from a noun to its parts? If a motorcycle is red, are its wheels red? Is a nuclear submarine's captain nuclear? These questions are easy for humans to judge using our commonsense understanding of the world, but are difficult for computers. To attack this challenge, we crowdsource a set of human judgments that answer the English-language question ``Given a whole described by an adjective, does the adjective also describe a given part?'' We build strong baselines for this task with a classification approach. Our findings indicate that, despite the recent successes of large language models on tasks aimed to assess commonsense knowledge, these models do not greatly outperform simple word-level models based on pre-trained word embeddings. This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings. Our dataset will serve as a useful testbed for future research in commonsense reasoning, especially as it relates to adjectives and objects\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis.\n \n \n \n \n\n\n \n Huang, X., May, J., & Peng, N.\n\n\n \n\n\n\n In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6394–6400, Hong Kong, China, November 2019. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"WhatPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{huang-etal-2019-matters,\n    title = "What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis",\n    author = "Huang, Xiaolei  and\n      May, Jonathan  and\n      Peng, Nanyun",\n    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",\n    month = nov,\n    year = "2019",\n    address = "Hong Kong, China",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/D19-1672",\n    doi = "10.18653/v1/D19-1672",\n    pages = "6394--6400",\n    abstract = "Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages, it is unclear what knowledge is transferred. In this paper, we first propose a simple and efficient neural architecture for cross-lingual NER. Experiments show that our model achieves competitive performance with the state-of-the-art. We further explore how transfer learning works for cross-lingual NER on two transferable factors: sequential order and multilingual embedding. Our results shed light on future research for improving cross-lingual NER.",\n}\n\n
\n
\n\n\n
\n Building named entity recognition (NER) models for languages that do not have much training data is a challenging task. While recent work has shown promising results on cross-lingual transfer from high-resource languages, it is unclear what knowledge is transferred. In this paper, we first propose a simple and efficient neural architecture for cross-lingual NER. Experiments show that our model achieves competitive performance with the state-of-the-art. We further explore how transfer learning works for cross-lingual NER on two transferable factors: sequential order and multilingual embedding. Our results shed light on future research for improving cross-lingual NER.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Cross-lingual Structure Transfer for Relation and Event Extraction.\n \n \n \n \n\n\n \n Subburathinam, A., Lu, D., Ji, H., May, J., Chang, S., Sil, A., & Voss, C.\n\n\n \n\n\n\n In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 313–325, Hong Kong, China, November 2019. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Cross-lingualPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{subburathinam-etal-2019-cross,\n    title = "Cross-lingual Structure Transfer for Relation and Event Extraction",\n    author = "Subburathinam, Ananya  and\n      Lu, Di  and\n      Ji, Heng  and\n      May, Jonathan  and\n      Chang, Shih-Fu  and\n      Sil, Avirup  and\n      Voss, Clare",\n    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",\n    month = nov,\n    year = "2019",\n    address = "Hong Kong, China",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/D19-1030",\n    doi = "10.18653/v1/D19-1030",\n    pages = "313--325",\n    abstract = "The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of cross-lingual structure transfer techniques for these tasks. We exploit relation- and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6{\\%} F-score for Relation Extraction, and 63.1{\\%} F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer.",\n}\n\n
\n
\n\n\n
\n The identification of complex semantic structures such as events and entity relations, already a challenging Information Extraction task, is doubly difficult from sources written in under-resourced and under-annotated languages. We investigate the suitability of cross-lingual structure transfer techniques for these tasks. We exploit relation- and event-relevant language-universal features, leveraging both symbolic (including part-of-speech and dependency path) and distributional (including type representation and contextualized representation) information. By representing all entity mentions, event triggers, and contexts into this complex and structured multilingual common space, using graph convolutional networks, we can train a relation or event extractor from source language annotations and apply it to the target language. Extensive experiments on cross-lingual relation and event transfer among English, Chinese, and Arabic demonstrate that our approach achieves performance comparable to state-of-the-art supervised models trained on up to 3,000 manually annotated mentions: up to 62.6% F-score for Relation Extraction, and 63.1% F-score for Event Argument Role Labeling. The event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese. We thus find that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Comprehensible Context-driven Text Game Playing.\n \n \n \n\n\n \n Yin, X., & May, J.\n\n\n \n\n\n\n August 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inprcoeedings{Yin2019ComprehensibleCT,\n  title={Comprehensible Context-driven Text Game Playing},\n  author={Xusen Yin and Jonathan May},\n  booktitle={Proc. 2019 IEEE Conference on Games (CoG)},\n  year={2019},\n  month=August,\n  pages={1-8}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage.\n \n \n \n \n\n\n \n Boschee, E., Barry, J., Billa, J., Freedman, M., Gowda, T., Lignos, C., Palen-Michel, C., Pust, M., Khonglah, B. K., Madikeri, S., May, J., & Miller, S.\n\n\n \n\n\n\n In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 19–24, Florence, Italy, July 2019. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"SARAL:Paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{boschee-etal-2019-saral,\n    title = "{SARAL}: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage",\n    author = "Boschee, Elizabeth  and\n      Barry, Joel  and\n      Billa, Jayadev  and\n      Freedman, Marjorie  and\n      Gowda, Thamme  and\n      Lignos, Constantine  and\n      Palen-Michel, Chester  and\n      Pust, Michael  and\n      Khonglah, Banriskhem Kayang  and\n      Madikeri, Srikanth  and\n      May, Jonathan  and\n      Miller, Scott",\n    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",\n    month = jul,\n    year = "2019",\n    address = "Florence, Italy",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/P19-3004",\n    doi = "10.18653/v1/P19-3004",\n    pages = "19--24",\n    abstract = "With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.",\n}\n\n
\n
\n\n\n
\n With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation.\n \n \n \n \n\n\n \n Pourdamghani, N., Aldarrab, N., Ghazvininejad, M., Knight, K., & May, J.\n\n\n \n\n\n\n In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3057–3062, Florence, Italy, July 2019. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"TranslatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{pourdamghani-etal-2019-translating,\n    title = "Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation",\n    author = "Pourdamghani, Nima  and\n      Aldarrab, Nada  and\n      Ghazvininejad, Marjan  and\n      Knight, Kevin  and\n      May, Jonathan",\n    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",\n    month = jul,\n    year = "2019",\n    address = "Florence, Italy",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/P19-1293",\n    doi = "10.18653/v1/P19-1293",\n    pages = "3057--3062",\n    abstract = "Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then {`}translating{'} the resulting pseudo-translation, or {`}Translationese{'} into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages. We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario.",\n}\n\n
\n
\n\n\n
\n Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation. In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then `translating' the resulting pseudo-translation, or `Translationese' into a fully fluent translation. We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages. We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Proceedings of the 13th International Workshop on Semantic Evaluation.\n \n \n \n \n\n\n \n May, J., Shutova, E., Herbelot, A., Zhu, X., Apidianaki, M., & Mohammad, S. M.,\n editors.\n \n\n\n \n\n\n\n Association for Computational Linguistics. Minneapolis, Minnesota, USA, June 2019.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@proceedings{semeval-2019-international,\n    title = "Proceedings of the 13th International Workshop on Semantic Evaluation",\n    editor = "May, Jonathan  and\n      Shutova, Ekaterina  and\n      Herbelot, Aurelie  and\n      Zhu, Xiaodan  and\n      Apidianaki, Marianna  and\n      Mohammad, Saif M.",\n    month = jun,\n    year = "2019",\n    address = "Minneapolis, Minnesota, USA",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/S19-2000",\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging.\n \n \n \n \n\n\n \n Huang, L., Ji, H., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3823–3833, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Cross-lingualPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{huang-ji-may:2019:N19-1,\n  author    = {Huang, Lifu  and  Ji, Heng  and  May, Jonathan},\n  title     = {Cross-lingual Multi-Level Adversarial Transfer to Enhance Low-Resource Name Tagging},\n  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},\n  month     = {June},\n  year      = {2019},\n  address   = {Minneapolis, Minnesota},\n  publisher = {Association for Computational Linguistics},\n  pages     = {3823--3833},\n  abstract  = {We focus on improving name tagging for low-resource languages using annotations from related languages. Previous studies either directly project annotations from a source language to a target language using cross-lingual representations or use a shared encoder in a multitask network to transfer knowledge. These approaches inevitably introduce noise to the target language annotation due to mismatched source-target sentence structures. To effectively transfer the resources, we develop a new neural architecture that leverages multi-level adversarial transfer: (1) word-level adversarial training, which projects source language words into the same semantic space as those of the target language without using any parallel corpora or bilingual gazetteers, and (2) sentence-level adversarial training, which yields language-agnostic sequential features. Our neural architecture outperforms previous approaches on CoNLL data sets. Moreover, on 10 low-resource languages, our approach achieves up to 16\\% absolute F-score gain over all high-performing baselines on cross-lingual transfer without using any target-language resources.},\n  url       = {http://www.aclweb.org/anthology/N19-1383}\n}\n\n
\n
\n\n\n
\n We focus on improving name tagging for low-resource languages using annotations from related languages. Previous studies either directly project annotations from a source language to a target language using cross-lingual representations or use a shared encoder in a multitask network to transfer knowledge. These approaches inevitably introduce noise to the target language annotation due to mismatched source-target sentence structures. To effectively transfer the resources, we develop a new neural architecture that leverages multi-level adversarial transfer: (1) word-level adversarial training, which projects source language words into the same semantic space as those of the target language without using any parallel corpora or bilingual gazetteers, and (2) sentence-level adversarial training, which yields language-agnostic sequential features. Our neural architecture outperforms previous approaches on CoNLL data sets. Moreover, on 10 low-resource languages, our approach achieves up to 16% absolute F-score gain over all high-performing baselines on cross-lingual transfer without using any target-language resources.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages.\n \n \n \n \n\n\n \n Cardenas, R., Lin, Y., Ji, H., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2428–2439, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{cardenas-EtAl:2019:N19-1,\n  author    = {Cardenas, Ronald  and  Lin, Ying  and  Ji, Heng  and  May, Jonathan},\n  title     = {A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages},\n  booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},\n  month     = {June},\n  year      = {2019},\n  address   = {Minneapolis, Minnesota},\n  publisher = {Association for Computational Linguistics},\n  pages     = {2428--2439},\n  abstract  = {Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to ground their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a `ciphertext' and seeks a POS tag-to-cluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger's utility by incorporating it into a true `zero-resource' variant of the MALOPA (Ammar et al., 2016) dependency parser model that removes the current reliance on multilingual resources and gold POS tags for new languages. Experiments show that including our tagger makes up much of the accuracy lost when gold POS tags are unavailable.},\n  url       = {http://www.aclweb.org/anthology/N19-1252}\n}\n\n
\n
\n\n\n
\n Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to ground their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervised POS tagging that yields fully grounded output and requires no labeled training data. We find the classic method of Brown et al. (1992) clusters well in our use case and employ a decipherment-based approach to grounding. This approach presumes a sequence of cluster IDs is a `ciphertext' and seeks a POS tag-to-cluster ID mapping that will reveal the POS sequence. We show intrinsically that, despite the difficulty of the task, we obtain reasonable performance across a variety of languages. We also show extrinsically that incorporating our POS tagger into a name tagger leads to state-of-the-art tagging performance in Sinhalese and Kinyarwanda, two languages with nearly no labeled POS data available. We further demonstrate our tagger's utility by incorporating it into a true `zero-resource' variant of the MALOPA (Ammar et al., 2016) dependency parser model that removes the current reliance on multilingual resources and gold POS tags for new languages. Experiments show that including our tagger makes up much of the accuracy lost when gold POS tags are unavailable.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2018\n \n \n (7)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Translating a Language You Don't Know In the Chinese Room.\n \n \n \n \n\n\n \n Hermjakob, U., May, J., Pust, M., & Knight, K.\n\n\n \n\n\n\n In Proceedings of ACL 2018, System Demonstrations, pages 62–67, Melbourne, Australia, July 2018. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"TranslatingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{hermjakob-EtAl:2018:Demos,\n  author    = {Hermjakob, Ulf  and  May, Jonathan  and  Pust, Michael  and  Knight, Kevin},\n  title     = {Translating a Language You Don't Know In the Chinese Room},\n  booktitle = {Proceedings of ACL 2018, System Demonstrations},\n  month     = {July},\n  year      = {2018},\n  address   = {Melbourne, Australia},\n  publisher = {Association for Computational Linguistics},\n  pages     = {62--67},\n  abstract  = {In a corruption of John Searle's famous AI thought experiment, the Chinese Room (Searle, 1980), we twist its original intent by enabling humans to translate text, e.g. from Uyghur to English, even if they don't have any prior knowledge of the source language. Our enabling tool, which we call the Chinese Room, is equipped with the same resources made available to a machine translation engine. We find that our superior language model and world knowledge allows us to create perfectly fluent and nearly adequate translations, with human expertise required only for the target language. The Chinese Room tool can be used to rapidly create small corpora of parallel data when bilingual translators are not readily available, in particular for low-resource languages.},\n  url       = {http://www.aclweb.org/anthology/P18-4011}\n}\n\n\n
\n
\n\n\n
\n In a corruption of John Searle's famous AI thought experiment, the Chinese Room (Searle, 1980), we twist its original intent by enabling humans to translate text, e.g. from Uyghur to English, even if they don't have any prior knowledge of the source language. Our enabling tool, which we call the Chinese Room, is equipped with the same resources made available to a machine translation engine. We find that our superior language model and world knowledge allows us to create perfectly fluent and nearly adequate translations, with human expertise required only for the target language. The Chinese Room tool can be used to rapidly create small corpora of parallel data when bilingual translators are not readily available, in particular for low-resource languages.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Out-of-the-box Universal Romanization Tool uroman.\n \n \n \n \n\n\n \n Hermjakob, U., May, J., & Knight, K.\n\n\n \n\n\n\n In Proceedings of ACL 2018, System Demonstrations, pages 13–18, Melbourne, Australia, July 2018. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Out-of-the-boxPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{hermjakob-may-knight:2018:Demos,\n  author    = {Hermjakob, Ulf  and  May, Jonathan  and  Knight, Kevin},\n  title     = {Out-of-the-box Universal Romanization Tool uroman},\n  booktitle = {Proceedings of ACL 2018, System Demonstrations},\n  month     = {July},\n  year      = {2018},\n  address   = {Melbourne, Australia},\n  publisher = {Association for Computational Linguistics},\n  pages     = {13--18},\n  abstract  = {We present uroman, a tool for converting text in myriads of languages and scripts such as Chinese, Arabic and Cyrillic into a common Latin-script representation. The tool relies on Unicode data and other tables, and handles nearly all character sets, including some that are quite obscure such as Tibetan and Tifinagh. uroman converts digital numbers in various scripts to Western Arabic numerals. Romanization enables the application of string-similarity metrics to texts from different scripts without the need and complexity of an intermediate phonetic representation. The tool is freely and publicly available as a Perl script suitable for inclusion in data processing pipelines and as an interactive demo web page.},\n  url       = {http://www.aclweb.org/anthology/P18-4003}\n}\n\n\n
\n
\n\n\n
\n We present uroman, a tool for converting text in myriads of languages and scripts such as Chinese, Arabic and Cyrillic into a common Latin-script representation. The tool relies on Unicode data and other tables, and handles nearly all character sets, including some that are quite obscure such as Tibetan and Tifinagh. uroman converts digital numbers in various scripts to Western Arabic numerals. Romanization enables the application of string-similarity metrics to texts from different scripts without the need and complexity of an intermediate phonetic representation. The tool is freely and publicly available as a Perl script suitable for inclusion in data processing pipelines and as an interactive demo web page.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Towards Controllable Story Generation.\n \n \n \n \n\n\n \n Peng, N., Ghazvininejad, M., May, J., & Knight, K.\n\n\n \n\n\n\n In Proceedings of the First Workshop on Storytelling, pages 43–49, New Orleans, Louisiana, June 2018. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"TowardsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{peng-EtAl:2018:W18-15,\n  author    = {Peng, Nanyun  and  Ghazvininejad, Marjan  and  May, Jonathan  and  Knight, Kevin},\n  title     = {Towards Controllable Story Generation},\n  booktitle = {Proceedings of the First Workshop on Storytelling},\n  month     = {June},\n  year      = {2018},\n  address   = {New Orleans, Louisiana},\n  publisher = {Association for Computational Linguistics},\n  pages     = {43--49},\n  abstract  = {We present a general framework of analyzing existing story corpora to generate controllable and creative new stories. The proposed framework needs little manual annotation to achieve controllable story generation. It creates a new interface for humans to interact with computers to generate personalized stories. We apply the framework to build recurrent neural network (RNN)-based generation models to control story ending valence and storyline. Experiments show that our methods successfully achieve the control and enhance the coherence of stories through introducing storylines. with additional control factors, the generation model gets lower perplexity, and yields more coherent stories that are faithful to the control factors according to human evaluation.},\n  url       = {http://www.aclweb.org/anthology/W18-1505}\n}\n\n\n
\n
\n\n\n
\n We present a general framework of analyzing existing story corpora to generate controllable and creative new stories. The proposed framework needs little manual annotation to achieve controllable story generation. It creates a new interface for humans to interact with computers to generate personalized stories. We apply the framework to build recurrent neural network (RNN)-based generation models to control story ending valence and storyline. Experiments show that our methods successfully achieve the control and enhance the coherence of stories through introducing storylines. with additional control factors, the generation model gets lower perplexity, and yields more coherent stories that are faithful to the control factors according to human evaluation.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Proceedings of The 12th International Workshop on Semantic Evaluation.\n \n \n \n \n\n\n \n Apidianaki, M., Mohammad, S. M., May, J., Shutova, E., Bethard, S., & Carpuat, M.,\n editors.\n \n\n\n \n\n\n\n Association for Computational Linguistics, New Orleans, Louisiana, June 2018.\n \n\n\n\n
\n\n\n\n \n \n \"ProceedingsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@Book{S18-1:2018,\n  editor    = {Marianna Apidianaki  and  Saif M. Mohammad  and  Jonathan May  and  Ekaterina Shutova  and  Steven Bethard  and  Marine Carpuat},\n  title     = {Proceedings of The 12th International Workshop on Semantic Evaluation},\n  month     = {June},\n  year      = {2018},\n  address   = {New Orleans, Louisiana},\n  publisher = {Association for Computational Linguistics},\n  url       = {http://www.aclweb.org/anthology/S18-1}\n}\n\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System.\n \n \n \n \n\n\n \n Zhang, B., Lin, Y., Pan, X., Lu, D., May, J., Knight, K., & Ji, H.\n\n\n \n\n\n\n In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 41–45, New Orleans, Louisiana, June 2018. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ELISA-EDL:Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{zhang-EtAl:2018:N18-5,\n  author    = {Zhang, Boliang  and  Lin, Ying  and  Pan, Xiaoman  and  Lu, Di  and  May, Jonathan  and  Knight, Kevin  and  Ji, Heng},\n  title     = {ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System},\n  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations},\n  month     = {June},\n  year      = {2018},\n  address   = {New Orleans, Louisiana},\n  publisher = {Association for Computational Linguistics},\n  pages     = {41--45},\n  abstract  = {We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap. We make all of our data sets, resources and system training and testing APIs publicly available for research purpose.},\n  url       = {http://www.aclweb.org/anthology/N18-5009}\n}\n\n
\n
\n\n\n
\n We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap. We make all of our data sets, resources and system training and testing APIs publicly available for research purpose.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Recurrent Neural Networks as Weighted Language Recognizers.\n \n \n \n \n\n\n \n Chen, Y., Gilroy, S., Maletti, A., May, J., & Knight, K.\n\n\n \n\n\n\n In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2261–2271, New Orleans, Louisiana, June 2018. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"RecurrentPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{chen-EtAl:2018:N18-14,\n  author    = {Chen, Yining  and  Gilroy, Sorcha  and  Maletti, Andreas  and  May, Jonathan  and  Knight, Kevin},\n  title     = {Recurrent Neural Networks as Weighted Language Recognizers},\n  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},\n  month     = {June},\n  year      = {2018},\n  address   = {New Orleans, Louisiana},\n  publisher = {Association for Computational Linguistics},\n  pages     = {2261--2271},\n  abstract  = {We investigate the computational complexity of various problems for simple recurrent neural networks (RNNs) as formal models for recognizing weighted languages. We focus on the single-layer, ReLU-activation, rational-weight RNNs with softmax, which are commonly used in natural language processing applications. We show that most problems for such RNNs are undecidable, including consistency, equivalence, minimization, and the determination of the highest-weighted string. However, for consistent RNNs the last problem becomes decidable, although the solution length can surpass all computable bounds. If additionally the string is limited to polynomial length, the problem becomes NP-complete. In summary, this shows that approximations and heuristic algorithms are necessary in practical applications of those RNNs.},\n  url       = {http://www.aclweb.org/anthology/N18-1205}\n}\n\n
\n
\n\n\n
\n We investigate the computational complexity of various problems for simple recurrent neural networks (RNNs) as formal models for recognizing weighted languages. We focus on the single-layer, ReLU-activation, rational-weight RNNs with softmax, which are commonly used in natural language processing applications. We show that most problems for such RNNs are undecidable, including consistency, equivalence, minimization, and the determination of the highest-weighted string. However, for consistent RNNs the last problem becomes decidable, although the solution length can surpass all computable bounds. If additionally the string is limited to polynomial length, the problem becomes NP-complete. In summary, this shows that approximations and heuristic algorithms are necessary in practical applications of those RNNs.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Incident-Driven Machine Translation and Name Tagging for Low-resource Languages.\n \n \n \n \n\n\n \n Hermjakob, U., Li, Q., Marcu, D., May, J., Mielke, S. J., Pourdamghani, N., Pust, M., Shi, X., Knight, K., Levinboim, T., Murray, K., Chiang, D., Zhang, B., Pan, X., Lu, D., Lin, Y., & Ji, H.\n\n\n \n\n\n\n Machine Translation, 32(1): 59–89. Jun 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Incident-DrivenPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@Article{Hermjakob2018,\nauthor="Hermjakob, Ulf\nand Li, Qiang\nand Marcu, Daniel\nand May, Jonathan\nand Mielke, Sebastian J.\nand Pourdamghani, Nima\nand Pust, Michael\nand Shi, Xing\nand Knight, Kevin\nand Levinboim, Tomer\nand Murray, Kenton\nand Chiang, David\nand Zhang, Boliang\nand Pan, Xiaoman\nand Lu, Di\nand Lin, Ying\nand Ji, Heng",\ntitle="Incident-Driven Machine Translation and Name Tagging for Low-resource Languages",\njournal="Machine Translation",\nyear="2018",\nmonth="Jun",\nday="01",\nvolume="32",\nnumber="1",\npages="59--89",\nabstract="We describe novel approaches to tackling the problem of natural language processing for low-resource languages. The approaches are embodied in systems for name tagging and machine translation (MT) that we constructed to participate in the NIST LoReHLT evaluation in 2016. Our methods include universal tools, rapid resource and knowledge acquisition, rapid language projection, and joint methods for MT and name tagging.",\nissn="1573-0573",\ndoi="10.1007/s10590-017-9207-1",\nurl="https://doi.org/10.1007/s10590-017-9207-1"\n}\n\n
\n
\n\n\n
\n We describe novel approaches to tackling the problem of natural language processing for low-resource languages. The approaches are embodied in systems for name tagging and machine translation (MT) that we constructed to participate in the NIST LoReHLT evaluation in 2016. Our methods include universal tools, rapid resource and knowledge acquisition, rapid language projection, and joint methods for MT and name tagging.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2017\n \n \n (4)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.\n \n \n \n \n\n\n \n Lifu, H., Jonathan, M., Xiaoman, P., Heng, J., Xiang, R., Jiawei, H., Lin, Z., & A., H. J.\n\n\n \n\n\n\n Big Data, 5(1): 19-31. 2017.\n PMID: 28328252\n\n\n\n
\n\n\n\n \n \n \"LiberalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{doi:10.1089/big.2017.0012,\nauthor = { Huang Lifu  and  May Jonathan  and  Pan Xiaoman  and  Ji Heng  and  Ren Xiang  and  Han Jiawei  and  Zhao Lin  and  Hendler James A. },\ntitle = {Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems},\njournal = {Big Data},\nvolume = {5},\nnumber = {1},\npages = {19-31},\nyear = {2017},\ndoi = {10.1089/big.2017.0012},\n    note ={PMID: 28328252},\n\nURL = { \n        https://doi.org/10.1089/big.2017.0012\n\n},\neprint = { \n        https://doi.org/10.1089/big.2017.0012\n\n}\n,\n    abstract = { Abstract The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework. }\n}\n\n
\n
\n\n\n
\n Abstract The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework. \n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation.\n \n \n \n \n\n\n \n May, J., & Priyadarshi, J.\n\n\n \n\n\n\n In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 536–545, Vancouver, Canada, August 2017. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"SemEval-2017Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{may-priyadarshi:2017:SemEval,\n  author    = {May, Jonathan  and  Priyadarshi, Jay},\n  title     = {SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation},\n  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},\n  month     = {August},\n  year      = {2017},\n  address   = {Vancouver, Canada},\n  publisher = {Association for Computational Linguistics},\n  pages     = {536--545},\n  abstract  = {In this report we summarize the results of the 2017 AMR SemEval shared task.\n\tThe task consisted of two separate yet related subtasks. In the parsing\n\tsubtask, participants were asked to produce Abstract Meaning Representation\n\t(AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the\n\tbiomedical domain. In the generation subtask, participants were asked to\n\tgenerate English sentences given AMR graphs in the news/forum domain. A total\n\tof five sites participated in the parsing subtask, and four participated in the\n\tgeneration subtask. \n\tAlong with a description of the task and the participants' systems, we show\n\tvarious score ablations and some sample outputs.},\n  url       = {http://www.aclweb.org/anthology/S17-2090}\n}\n\n
\n
\n\n\n
\n In this report we summarize the results of the 2017 AMR SemEval shared task. The task consisted of two separate yet related subtasks. In the parsing subtask, participants were asked to produce Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the biomedical domain. In the generation subtask, participants were asked to generate English sentences given AMR graphs in the news/forum domain. A total of five sites participated in the parsing subtask, and four participated in the generation subtask. Along with a description of the task and the participants' systems, we show various score ablations and some sample outputs.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Cross-lingual Name Tagging and Linking for 282 Languages.\n \n \n \n \n\n\n \n Pan, X., Zhang, B., May, J., Nothman, J., Knight, K., & Ji, H.\n\n\n \n\n\n\n In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1946–1958, Vancouver, Canada, July 2017. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Cross-lingualPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{pan-EtAl:2017:Long2,\n  author    = {Pan, Xiaoman  and  Zhang, Boliang  and  May, Jonathan  and  Nothman, Joel  and  Knight, Kevin  and  Ji, Heng},\n  title     = {Cross-lingual Name Tagging and Linking for 282 Languages},\n  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n  month     = {July},\n  year      = {2017},\n  address   = {Vancouver, Canada},\n  publisher = {Association for Computational Linguistics},\n  pages     = {1946--1958},\n  abstract  = {The ambitious goal of this work is to develop a cross-lingual name tagging and\n\tlinking framework for 282 languages that exist in Wikipedia. Given a document\n\tin any of these languages, our framework is able to identify name mentions,\n\tassign a coarse-grained or fine-grained type to each mention, and link it to an\n\tEnglish Knowledge Base (KB) if it is linkable. We achieve this goal by\n\tperforming a series of new KB mining methods: generating ``silver-standard''\n\tannotations by transferring annotations from English to other languages through\n\tcross-lingual links and KB properties, refining annotations through\n\tself-training and topic selection, deriving language-specific morphology\n\tfeatures from anchor links, and mining word translation pairs from\n\tcross-lingual links. Both name tagging and linking results for 282 languages\n\tare promising on Wikipedia data and on-Wikipedia data.},\n  url       = {http://aclweb.org/anthology/P17-1178}\n}\n\n
\n
\n\n\n
\n The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating ``silver-standard'' annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Team ELISA System for DARPA LORELEI Speech Evaluation 2016.\n \n \n \n \n\n\n \n Papadopoulos, P., Travadi, R., Vaz, C., Malandrakis, N., Hermjakob, U., Pourdamghani, N., Pust, M., Zhang, B., Pan, X., Lu, D., Lin, Y., Glembek, O., Baskar, M. K., Karafiát, M., Burget, L., Hasegawa-Johnson, M., Ji, H., May, J., Knight, K., & Narayanan, S. S.\n\n\n \n\n\n\n In Proc. Interspeech 2017, pages 2053–2057, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"TeamPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{Papadopoulos2017,\n  author={Pavlos Papadopoulos and Ruchir Travadi and Colin Vaz and Nikolaos Malandrakis and Ulf Hermjakob and Nima Pourdamghani and Michael Pust and Boliang Zhang and Xiaoman Pan and Di Lu and Ying Lin and Ondřej Glembek and Murali Karthick Baskar and Martin Karafiát and Lukáš Burget and Mark Hasegawa-Johnson and Heng Ji and Jonathan May and Kevin Knight and Shrikanth S. Narayanan},\n  title={Team ELISA System for DARPA LORELEI Speech Evaluation 2016},\n  year=2017,\n  booktitle={Proc. Interspeech 2017},\n  pages={2053--2057},\n  doi={10.21437/Interspeech.2017-180},\n  url={http://dx.doi.org/10.21437/Interspeech.2017-180}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2016\n \n \n (4)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Transfer Learning for Low-Resource Neural Machine Translation.\n \n \n \n \n\n\n \n Zoph, B., Yuret, D., May, J., & Knight, K.\n\n\n \n\n\n\n In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1568–1575, Austin, Texas, November 2016. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"TransferPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{zoph-EtAl:2016:EMNLP2016,\n  author    = {Zoph, Barret  and  Yuret, Deniz  and  May, Jonathan  and  Knight, Kevin},\n  title     = {Transfer Learning for Low-Resource Neural Machine Translation},\n  booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},\n  month     = {November},\n  year      = {2016},\n  address   = {Austin, Texas},\n  publisher = {Association for Computational Linguistics},\n  pages     = {1568--1575},\n  url       = {https://aclweb.org/anthology/D16-1163}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n SemEval-2016 Task 8: Meaning Representation Parsing.\n \n \n \n \n\n\n \n May, J.\n\n\n \n\n\n\n In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 1063–1073, San Diego, California, June 2016. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"SemEval-2016Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{may:2016:SemEval,\n  author    = {May, Jonathan},\n  title     = {SemEval-2016 Task 8: Meaning Representation Parsing},\n  booktitle = {Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)},\n  month     = {June},\n  year      = {2016},\n  address   = {San Diego, California},\n  publisher = {Association for Computational Linguistics},\n  pages     = {1063--1073},\n  url       = {http://www.aclweb.org/anthology/S16-1166}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies.\n \n \n \n \n\n\n \n Zoph, B., Vaswani, A., May, J., & Knight, K.\n\n\n \n\n\n\n In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1217–1222, San Diego, California, June 2016. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"Simple,Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{zoph-EtAl:2016:N16-1,\n  author    = {Zoph, Barret  and  Vaswani, Ashish  and  May, Jonathan  and  Knight, Kevin},\n  title     = {Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies},\n  booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},\n  month     = {June},\n  year      = {2016},\n  address   = {San Diego, California},\n  publisher = {Association for Computational Linguistics},\n  pages     = {1217--1222},\n  url       = {http://www.aclweb.org/anthology/N16-1145}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Extracting Structured Scholarly Information from the Machine Translation Literature.\n \n \n \n\n\n \n Choi, E., Horvat, M., May, J., Knight, K., & Marcu, D.\n\n\n \n\n\n\n In Chair), N. C. (., Choukri, K., Declerck, T., Goggi, S., Grobelnik, M., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., & Piperidis, S., editor(s), Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France, may 2016. European Language Resources Association (ELRA)\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{CHOI16.581,\n  author = {Eunsol Choi and Matic Horvat and Jonathan May and Kevin Knight and Daniel Marcu},\n  title = {Extracting Structured Scholarly Information from the Machine Translation Literature},\n  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},\n  year = {2016},\n  month = {may},\n  date = {23-28},\n  location = {Portorož, Slovenia},\n  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},\n  publisher = {European Language Resources Association (ELRA)},\n  address = {Paris, France},\n  isbn = {978-2-9517408-9-1},\n  language = {english}\n }\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2015\n \n \n (4)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation.\n \n \n \n \n\n\n \n Pust, M., Hermjakob, U., Knight, K., Marcu, D., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1143–1154, Lisbon, Portugal, September 2015. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ParsingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{pust-EtAl:2015:EMNLP,\n  author    = {Pust, Michael  and  Hermjakob, Ulf  and  Knight, Kevin  and  Marcu, Daniel  and  May, Jonathan},\n  title     = {Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation},\n  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},\n  month     = {September},\n  year      = {2015},\n  address   = {Lisbon, Portugal},\n  publisher = {Association for Computational Linguistics},\n  pages     = {1143--1154},\n  url       = {http://aclweb.org/anthology/D15-1136}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n A Corpus of Rich Metaphor Annotation.\n \n \n \n \n\n\n \n Gordon, J., Hobbs, J., May, J., Mohler, M., Morbini, F., Rink, B., Tomlinson, M., & Wertheim, S.\n\n\n \n\n\n\n In Proceedings of the Third Workshop on Metaphor in NLP, pages 56–66, Denver, Colorado, June 2015. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{gordon-EtAl:2015:Metaphor2,\n  author    = {Gordon, Jonathan  and  Hobbs, Jerry  and  May, Jonathan  and  Mohler, Michael  and  Morbini, Fabrizio  and  Rink, Bryan  and  Tomlinson, Marc  and  Wertheim, Suzanne},\n  title     = {A Corpus of Rich Metaphor Annotation},\n  booktitle = {Proceedings of the Third Workshop on Metaphor in NLP},\n  month     = {June},\n  year      = {2015},\n  address   = {Denver, Colorado},\n  publisher = {Association for Computational Linguistics},\n  pages     = {56--66},\n  url       = {http://www.aclweb.org/anthology/W15-1407}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n High-Precision Abductive Mapping of Multilingual Metaphors.\n \n \n \n \n\n\n \n Gordon, J., Hobbs, J., May, J., & Morbini, F.\n\n\n \n\n\n\n In Proceedings of the Third Workshop on Metaphor in NLP, pages 50–55, Denver, Colorado, June 2015. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"High-PrecisionPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{gordon-EtAl:2015:Metaphor1,\n  author    = {Gordon, Jonathan  and  Hobbs, Jerry  and  May, Jonathan  and  Morbini, Fabrizio},\n  title     = {High-Precision Abductive Mapping of Multilingual Metaphors},\n  booktitle = {Proceedings of the Third Workshop on Metaphor in NLP},\n  month     = {June},\n  year      = {2015},\n  address   = {Denver, Colorado},\n  publisher = {Association for Computational Linguistics},\n  pages     = {50--55},\n  url       = {http://www.aclweb.org/anthology/W15-1406}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation.\n \n \n \n \n\n\n \n Pust, M., Hermjakob, U., Knight, K., Marcu, D., & May, J.\n\n\n \n\n\n\n CoRR, abs/1504.06665. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"UsingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{DBLP:journals/corr/PustHKMM15,\n  author    = {Michael Pust and\n               Ulf Hermjakob and\n               Kevin Knight and\n               Daniel Marcu and\n               Jonathan May},\n  title     = {Using Syntax-Based Machine Translation to Parse English into Abstract\n               Meaning Representation},\n  journal   = {CoRR},\n  volume    = {abs/1504.06665},\n  year      = {2015},\n  url       = {http://arxiv.org/abs/1504.06665},\n  archivePrefix = {arXiv},\n  eprint    = {1504.06665},\n  timestamp = {Mon, 13 Aug 2018 16:48:21 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/PustHKMM15},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2014\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n An Arabizi-English Social Media Statistical Machine Translation System.\n \n \n \n\n\n \n May, J., Benjira, Y., & Echihabi, A.\n\n\n \n\n\n\n In Proceedings of the Eleventh Biennial Conference of the Association for Machine Translation in the Americas, Vancouver, Canada, October 2014. Association for Machine Translation in the Americas\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{may-benjira-echihabi:2014:AMTA,\n  author    = {May, Jonathan  and  Benjira, Yassine and Echihabi, Abdessamad},\n  title     = {An Arabizi-English Social Media Statistical Machine Translation System},\n  booktitle = {Proceedings of the Eleventh Biennial Conference of the\nAssociation for Machine Translation in the Americas},\n  month     = {October},\n  year      = {2014},\n  address   = {Vancouver, Canada},\n  publisher = {Association for Machine Translation in the Americas},\n}\n\n% missing: "Identifying Useful Human Correction Feedback from an On-line Machine Translation Service", (A. Barrón-Cedeño, L. Màrquez, L., C. Henríquez Q., L. Formiga, E. Romero, & J. May), Proc. IJCAI, 2013.\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2013\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Models of Translation Competitions.\n \n \n \n \n\n\n \n Hopkins, M., & May, J.\n\n\n \n\n\n\n In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1416–1424, Sofia, Bulgaria, August 2013. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ModelsPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{hopkins-may:2013:ACL2013,\n  author    = {Hopkins, Mark  and  May, Jonathan},\n  title     = {Models of Translation Competitions},\n  booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n  month     = {August},\n  year      = {2013},\n  address   = {Sofia, Bulgaria},\n  publisher = {Association for Computational Linguistics},\n  pages     = {1416--1424},\n  url       = {http://www.aclweb.org/anthology/P13-1139}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2012\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n An Analysis (and an Annotated Corpus) of User Responses to Machine Translation Output.\n \n \n \n\n\n \n Pighin, D., Màrquez, L., & May, J.\n\n\n \n\n\n\n In Chair), N. C. (., Choukri, K., Declerck, T., Doğan, M. U., Maegaard, B., Mariani, J., Odijk, J., & Piperidis, S., editor(s), Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12), Istanbul, Turkey, may 2012. European Language Resources Association (ELRA)\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{PIGHIN12.337,\n  author = {Daniele Pighin and Lluís Màrquez and Jonathan May},\n  title = {An Analysis (and an Annotated Corpus) of User Responses to Machine Translation Output},\n  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},\n  year = {2012},\n  month = {may},\n  date = {23-25},\n  address = {Istanbul, Turkey},\n  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},\n  publisher = {European Language Resources Association (ELRA)},\n  isbn = {978-2-9517408-7-7},\n  language = {english}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2011\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Tuning as Ranking.\n \n \n \n \n\n\n \n Hopkins, M., & May, J.\n\n\n \n\n\n\n In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 1352–1362, Edinburgh, Scotland, UK., July 2011. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"TuningPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{hopkins-may:2011:EMNLP,\n  author    = {Hopkins, Mark  and  May, Jonathan},\n  title     = {Tuning as Ranking},\n  booktitle = {Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing},\n  month     = {July},\n  year      = {2011},\n  address   = {Edinburgh, Scotland, UK.},\n  publisher = {Association for Computational Linguistics},\n  pages     = {1352--1362},\n  url       = {http://www.aclweb.org/anthology/D11-1125}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2010\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Efficient Inference through Cascades of Weighted Tree Transducers.\n \n \n \n \n\n\n \n May, J., Knight, K., & Vogler, H.\n\n\n \n\n\n\n In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1058–1066, Uppsala, Sweden, July 2010. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"EfficientPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{may-knight-vogler:2010:ACL,\n  author    = {May, Jonathan  and  Knight, Kevin  and  Vogler, Heiko},\n  title     = {Efficient Inference through Cascades of Weighted Tree Transducers},\n  booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},\n  month     = {July},\n  year      = {2010},\n  address   = {Uppsala, Sweden},\n  publisher = {Association for Computational Linguistics},\n  pages     = {1058--1066},\n  url       = {http://www.aclweb.org/anthology/P10-1108}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Re-structuring, Re-labeling, and Re-aligning for Syntax-Based Machine Translation .\n \n \n \n\n\n \n Wang, W., May, J., Knight, K., & Marcu, D.\n\n\n \n\n\n\n Computational Linguistics, 36(2): 247–277. June 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@Article{wangmayknightmarcu10:cl,\n  author = \t {Wei Wang and Jonathan May and Kevin Knight and Daniel Marcu},\n  title = \t {Re-structuring, Re-labeling, and Re-aligning for Syntax-Based Machine Translation },\n  journal = \t {Computational Linguistics},\n  year = \t 2010,\n  volume =\t 36,\n  number =\t 2,\n  pages =\t {247--277},\n  month =\t {June}\n}\n\n% missing: "Determinization of Weighted Tree Automata using Factorizations", (M. Büchse, J. May, and H. Vogler), Proc. FSMNLP, 2009.\n% missing: "Backward and Forward Bisimulation Minimization of Tree Automata", (J. Högberg, A. Maletti, and J. May), Theoretical Computer Science, volume 410, no. 37 (September, 2009).\n% missing: "Applications of Weighted Automata in Natural Language Processing", (K. Knight and J. May), Handbook of Weighted Automata (M. Droste, W. Kuich, H. Vogler, eds.), 2009. \n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2008\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Training Tree Transducers.\n \n \n \n\n\n \n Graehl, J., Knight, K., & May, J.\n\n\n \n\n\n\n Computational Linguistics, 34(3): 391–427. September 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@Article{graehlknightmay08:cl,\n  author = \t {Jonathan Graehl and Kevin Knight and Jonathan May},\n  title = \t {Training Tree Transducers},\n  journal = \t {Computational Linguistics},\n  year = \t 2008,\n  volume =\t 34,\n  number =\t 3,\n  pages =\t {391--427},\n  month =\t {September}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2007\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Syntactic Re-Alignment Models for Machine Translation.\n \n \n \n\n\n \n May, J., & Knight, K.\n\n\n \n\n\n\n In Eisner, J., & Kudo, T., editor(s), Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 360–368, Prague, Czech Republic, June 28 – June 30 2007. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{mayknight07:emnlp,\n  author = \t {Jonathan May and Kevin Knight},\n  title = \t {Syntactic Re-Alignment Models for Machine Translation},\n  booktitle =\t {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},\n  pages =\t {360--368},\n  year =\t 2007,\n  editor =\t {Jason Eisner and Taku Kudo},\n  address =\t {Prague, Czech Republic},\n  month =\t {June 28 -- June 30},\n  publisher =\t {Association for Computational Linguistics}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Backward and Forward Bisimulation Minimisation of Weighted Tree Automata.\n \n \n \n\n\n \n Högberg, J., Maletti, A., & May, J.\n\n\n \n\n\n\n In Holub, J., & Ždárek, J., editor(s), Proceedings of the 12th International Conference on Implementation and Application of Automata, CIAA 2007, volume 4783, of Lecture Notes in Computer Science, pages 109–121, Prague, Czech Republic, July 16–18 2007. Springer-Verlag\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{hogbergmalettimay07:ciaa,\n  author = \t {Johanna H\\"{o}gberg and Andreas Maletti and Jonathan May},\n  title = \t {Backward and Forward Bisimulation Minimisation of Weighted Tree Automata},\n  booktitle =\t {Proceedings of the 12th International Conference on Implementation and Application of Automata, CIAA 2007},\n  pages =\t {109--121},\n  year =\t 2007,\n  editor =\t {Jan Holub and Jan \\v{Z}d\\'{a}rek},\n  volume =\t 4783,\n  series =\t {Lecture Notes in Computer Science},\n  address =\t {Prague, Czech Republic},\n  month =\t {July 16--18},\n  publisher =\t {Springer-Verlag}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Bisimulation Minimisation for Weighted Tree Automata.\n \n \n \n\n\n \n Högberg, J., Maletti, A., & May, J.\n\n\n \n\n\n\n In Harju, T., Karhumäki, J., & Lepistö, A., editor(s), Proceedings of the 11th International Conference on Developments in Language Theory, DLT 2007, volume 4588, of Lecture Notes in Computer Science, pages 229–240, Turku, Finland, July 3–6 2007. Springer-Verlag\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{hogbergmalettimay07:dlt,\n  author = \t {Johanna H\\"{o}gberg and Andreas Maletti and Jonathan May},\n  title = \t {Bisimulation Minimisation for Weighted Tree Automata},\n  booktitle =\t {Proceedings of the 11th International Conference on Developments in Language Theory, DLT 2007},\n  pages =\t {229--240},\n  year =\t 2007,\n  editor =\t {Tero Harju and Juhani Karhum\\"{a}ki and Arto Lepist\\"{o}},\n  volume =\t 4588,\n  series =\t {Lecture Notes in Computer Science},\n  address =\t {Turku, Finland},\n  month =\t {July 3--6},\n  publisher =\t {Springer-Verlag}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2006\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Tiburon: A Weighted Tree Automata Toolkit.\n \n \n \n\n\n \n May, J., & Knight, K.\n\n\n \n\n\n\n In Ibarra, O. H., & Yen, H., editor(s), Proceedings of the 11th International Conference of Implementation and Application of Automata, CIAA 2006, volume 4094, of Lecture Notes in Computer Science, pages 102–113, Taipei, Taiwan, August 2006. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{mayknight06:ciaa,\n  author = \t {Jonathan May and Kevin Knight},\n  title = \t {Tiburon: A Weighted Tree Automata Toolkit},\n  booktitle =\t {Proceedings of the 11th International Conference of Implementation and Application of Automata, CIAA 2006},\n  pages =\t {102--113},\n  year =\t 2006,\n  editor =\t {Oscar H. Ibarra and Hsu-Chun Yen},\n  volume =\t 4094,\n  series =\t {Lecture Notes in Computer Science},\n  address =\t {Taipei, Taiwan},\n  month =\t {August},\n  publisher =\t {Springer}\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n A Better N-Best List: Practical Determinization of Weighted Finite Tree Automata.\n \n \n \n\n\n \n May, J., & Knight, K.\n\n\n \n\n\n\n In Khudanpur, S., & Roark, B., editor(s), Proceedings of the 2006 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, volume Main Proceedings, pages 351–358, Brooklyn, New York, June 5 – June 7 2006. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@InProceedings{mayknight06:naacl,\n  author = \t {Jonathan May and Kevin Knight},\n  title = \t {A Better N-Best List: Practical Determinization of Weighted Finite Tree Automata},\n  booktitle =\t {Proceedings of the 2006 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics},\n  pages =\t {351--358},\n  year =\t 2006,\n  editor =\t {Sanjeev Khudanpur and Brian Roark},\n  volume =\t {Main Proceedings},\n  address =\t {Brooklyn, New York},\n  month =\t {June 5 -- June 7},\n  publisher =\t {Association for Computational Linguistics}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2003\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Surprise! What's in a Cebuano or Hindi Name?.\n \n \n \n\n\n \n May, J., Brunstein, A., Natarajan, P., & Weischedel, R.\n\n\n \n\n\n\n ACM Transactions on Asian Language Information Processing (TALIP), 2(3): 169–180. 2003.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{979873,\n author = {Jonathan May and Ada Brunstein and Prem Natarajan and Ralph Weischedel},\n title = {Surprise! What's in a Cebuano or Hindi Name?},\n journal = {ACM Transactions on Asian Language Information Processing (TALIP)},\n volume = {2},\n number = {3},\n year = {2003},\n issn = {1530-0226},\n pages = {169--180},\n doi = {http://doi.acm.org/10.1145/979872.979873},\n publisher = {ACM Press},\n address = {New York, NY, USA},\n }\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n Answer Selection and Confidence Estimation.\n \n \n \n\n\n \n Xu, J., Licuanan, A., May, J., Miller, S., & Weischedel, R. M.\n\n\n \n\n\n\n In New Directions in Question Answering, pages 134-137, 2003. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{DBLP:conf/ndqa/XuLMMW03,\n  author    = {Jinxi Xu and\n               Ana Licuanan and\n               Jonathan May and\n               Scott Miller and\n               Ralph M. Weischedel},\n  title     = {Answer Selection and Confidence Estimation.},\n  booktitle = {New Directions in Question Answering},\n  year      = {2003},\n  pages     = {134-137},\n  crossref  = {DBLP:conf/ndqa/2003},\n  bibsource = {DBLP, http://dblp.uni-trier.de}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2002\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n TREC 2002 QA at BBN: Answer Selection and Confidence Estimation.\n \n \n \n \n\n\n \n Xu, J., Licuanan, A., May, J., Miller, S., & Weischedel, R. M.\n\n\n \n\n\n\n In TREC, 2002. \n \n\n\n\n
\n\n\n\n \n \n \"TRECLink\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{DBLP:conf/trec/XuLMMW02,\n  author    = {Jinxi Xu and\n               Ana Licuanan and\n               Jonathan May and\n               Scott Miller and\n               Ralph M. Weischedel},\n  title     = {TREC 2002 QA at BBN: Answer Selection and Confidence Estimation.},\n  booktitle = {TREC},\n  year      = {2002},\n  ee        = {http://trec.nist.gov/pubs/trec11/papers/bbn.xu.qa.pdf},\n  bibsource = {DBLP, http://dblp.uni-trier.de}\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);