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%3A%2F%2Fshirawein.github.io%2Fpubs.bib&fullnames=1&jsonp=1&owner=Wein&showSearch=true&theme=dividers&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=https%3A%2F%2Fshirawein.github.io%2Fpubs.bib&fullnames=1&jsonp=1&owner=Wein&showSearch=true&theme=dividers\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/show?bib=https%3A%2F%2Fshirawein.github.io%2Fpubs.bib&fullnames=1&jsonp=1&owner=Wein&showSearch=true&theme=dividers\"></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 2024\n \n \n (6)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n Development and Evaluation of Cross-lingual Abstract Meaning Representation.\n \n \n \n\n\n \n Shira Wein.\n\n\n \n\n\n\n Ph.D. Thesis, Washington, D.C., February 2024.\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 
\n
\n\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detection.\n  \n  \n  \n  \n\n\n  \n   Michael Regan;  Shira Wein;  George Baker;  and  Emilio Monti.\n\n\n  \n\n\n\n  In Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024), pages 1–17, Mexico City, Mexico, June 2024. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"MASSIVEPaper\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    3 downloads\n  \n  \n\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@inproceedings{regan-etal-2024-massive,\n    title = "{MASSIVE} Multilingual {A}bstract {M}eaning {R}epresentation: A Dataset and Baselines for Hallucination Detection",\n    author = "Regan, Michael  and\n      Wein, Shira  and\n      Baker, George  and\n      Monti, Emilio",\n    booktitle = "Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)",\n    month = jun,\n    year = "2024",\n    address = "Mexico City, Mexico",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2024.starsem-1.1",\n    pages = "1--17",\n    abstract = "Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. There has been substantial work developing AMR corpora in English and more recently across languages, though the limited size of existing datasets and the cost of collecting more annotations are prohibitive. With both engineering and scientific questions in mind, we introduce MASSIVE-AMR, a dataset with more than 84,000 text-to-graph annotations, currently the largest and most diverse of its kind: AMR graphs for 1,685 information-seeking utterances mapped to 50+ typologically diverse languages. We describe how we built our resource and its unique features before reporting on experiments using large language models for multilingual AMR and SPARQL parsing as well as applying AMRs for hallucination detection in the context of knowledge base question answering, with results shedding light on persistent issues using LLMs for structured parsing.",\n}\n
\n
\n\n\n
\n  Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance. There has been substantial work developing AMR corpora in English and more recently across languages, though the limited size of existing datasets and the cost of collecting more annotations are prohibitive. With both engineering and scientific questions in mind, we introduce MASSIVE-AMR, a dataset with more than 84,000 text-to-graph annotations, currently the largest and most diverse of its kind: AMR graphs for 1,685 information-seeking utterances mapped to 50+ typologically diverse languages. We describe how we built our resource and its unique features before reporting on experiments using large language models for multilingual AMR and SPARQL parsing as well as applying AMRs for hallucination detection in the context of knowledge base question answering, with results shedding light on persistent issues using LLMs for structured parsing.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Natural Language Processing RELIES on Linguistics.\n  \n  \n  \n  \n\n\n  \n   Juri Opitz;  Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  arXiv preprint arXiv:2405.05966.  2024.\n  \n\n\n\n
\n\n\n\n  \n  \n    \"NaturalPaper\n   \n  \n\n  \n\n  \n  link\n   \n  \n\n  bibtex\n    \n\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  
@article{opitz2024natural,\n  title={Natural Language Processing RELIES on Linguistics},\n  author={Opitz, Juri and Wein, Shira and Schneider, Nathan},\n  journal={arXiv preprint arXiv:2405.05966},\n  year={2024},\n  url = "https://arxiv.org/abs/2405.05966"\n}\n\n
\n
\n\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Barriers to Effective Evaluation of Simultaneous Interpretation.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  Te I;  Colin Cherry;  Juraj Juraska;  Dirk Padfield;  and  Wolfgang Macherey.\n\n\n  \n\n\n\n  In Findings of the Association for Computational Linguistics: EACL 2024, pages 209–219, St. Julian's, Malta, March 2024. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"BarriersPaper\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    3 downloads\n  \n  \n\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@inproceedings{wein-etal-2024-barriers,\n    title = "Barriers to Effective Evaluation of Simultaneous Interpretation",\n    author = "Wein, Shira  and\n      I, Te  and\n      Cherry, Colin  and\n      Juraska, Juraj  and\n      Padfield, Dirk  and\n      Macherey, Wolfgang",\n    booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",\n    month = mar,\n    year = "2024",\n    address = "St. Julian{'}s, Malta",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2024.findings-eacl.15",\n    pages = "209--219",\n    abstract = "Simultaneous interpretation is an especially challenging form of translation because it requires converting speech from one language to another in real-time. Though prior work has relied on out-of-the-box machine translation metrics to evaluate interpretation data, we hypothesize that strategies common in high-quality human interpretations, such as summarization, may not be handled well by standard machine translation metrics. In this work, we examine both qualitatively and quantitatively four potential barriers to evaluation of interpretation: disfluency, summarization, paraphrasing, and segmentation. Our experiments reveal that, while some machine translation metrics correlate fairly well with human judgments of interpretation quality, much work is still needed to account for strategies of interpretation during evaluation. As a first step to address this, we develop a fine-tuned model for interpretation evaluation, and achieve better correlation with human judgments than the state-of-the-art machine translation metrics.",\n}\n\n
\n
\n\n\n
\n  Simultaneous interpretation is an especially challenging form of translation because it requires converting speech from one language to another in real-time. Though prior work has relied on out-of-the-box machine translation metrics to evaluate interpretation data, we hypothesize that strategies common in high-quality human interpretations, such as summarization, may not be handled well by standard machine translation metrics. In this work, we examine both qualitatively and quantitatively four potential barriers to evaluation of interpretation: disfluency, summarization, paraphrasing, and segmentation. Our experiments reveal that, while some machine translation metrics correlate fairly well with human judgments of interpretation quality, much work is still needed to account for strategies of interpretation during evaluation. As a first step to address this, we develop a fine-tuned model for interpretation evaluation, and achieve better correlation with human judgments than the state-of-the-art machine translation metrics.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 753–765, St. Julian's, Malta, March 2024. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"LostPaper\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    15 downloads\n  \n  \n\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@inproceedings{wein-schneider-2024-lost,\n    title = "Lost in Translationese? Reducing Translation Effect Using {A}bstract {M}eaning {R}epresentation",\n    author = "Wein, Shira  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = mar,\n    year = "2024",\n    address = "St. Julian{'}s, Malta",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2024.eacl-long.45",\n    pages = "753--765",\n    abstract = "Translated texts bear several hallmarks distinct from texts originating in the language ({``}translationese{''}). Though individual translated texts are often fluent and preserve meaning, at a large scale, translated texts have statistical tendencies which distinguish them from text originally written in the language and can affect model performance. We frame the novel task of translationese reduction and hypothesize that Abstract Meaning Representation (AMR), a graph-based semantic representation which abstracts away from the surface form, can be used as an interlingua to reduce the amount of translationese in translated texts. By parsing English translations into an AMR and then generating text from that AMR, the result more closely resembles originally English text across three quantitative macro-level measures, without severely compromising fluency or adequacy. We compare our AMR-based approach against three other techniques based on machine translation or paraphrase generation. This work represents the first approach to reducing translationese in text and highlights the promise of AMR, given that our AMR-based approach outperforms more computationally intensive methods.",\n}\n\n
\n
\n\n\n
\n  Translated texts bear several hallmarks distinct from texts originating in the language (``translationese''). Though individual translated texts are often fluent and preserve meaning, at a large scale, translated texts have statistical tendencies which distinguish them from text originally written in the language and can affect model performance. We frame the novel task of translationese reduction and hypothesize that Abstract Meaning Representation (AMR), a graph-based semantic representation which abstracts away from the surface form, can be used as an interlingua to reduce the amount of translationese in translated texts. By parsing English translations into an AMR and then generating text from that AMR, the result more closely resembles originally English text across three quantitative macro-level measures, without severely compromising fluency or adequacy. We compare our AMR-based approach against three other techniques based on machine translation or paraphrase generation. This work represents the first approach to reducing translationese in text and highlights the promise of AMR, given that our AMR-based approach outperforms more computationally intensive methods.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Assessing the Cross-linguistic Utility of Abstract Meaning Representation.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  Computational Linguistics,1-55.  2024.\n  \n\n\n\n
\n\n\n\n  \n  \n    \"AssessingPaper\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{wein2024assessing,\n    author = {Wein, Shira and Schneider, Nathan},\n    title = "{Assessing the Cross-linguistic Utility of Abstract Meaning Representation}",\n    journal = {Computational Linguistics},\n    pages = {1-55},\n    year = {2024},\n    abstract = "{Semantic representations capture the meaning of a text. Abstract Meaning Representation (AMR), a type of semantic representation, focuses on predicate-argument structure and abstracts away from surface form. Though AMR was developed initially for English, it has now been adapted to a multitude of languages in the form of non-English annotation schemas, cross-lingual text-to-AMR parsing, and AMR-to-(non-English) text generation. We advance prior work on cross-lingual AMR by thoroughly investigating the amount, types, and causes of differences which appear in AMRs of different languages. Further, we compare how AMR captures meaning in cross-lingual pairs versus strings, and show that AMR graphs are able to draw out fine-grained differences between parallel sentences. We explore three primary research questions: (1) What are the types and causes of differences in parallel AMRs? (2) How can we measure the amount of difference between AMR pairs in different languages? (3) Given that AMR structure is affected by language and exhibits cross-lingual differences, how do cross-lingual AMR pairs compare to string-based representations of cross-lingual sentence pairs? We find that the source language itself does have a measurable impact on AMR structure, and that translation divergences and annotator choices also lead to differences in cross-lingual AMR pairs. We explore the implications of this finding throughout our study, concluding that, while AMR is useful to capture meaning across languages, evaluations need to take into account source language influences if they are to paint an accurate picture of system output, and meaning generally.}",\n    issn = {0891-2017},\n    doi = {10.1162/coli_a_00503},\n    url = {https://doi.org/10.1162/coli\\_a\\_00503},\n    eprint = {https://direct.mit.edu/coli/article-pdf/doi/10.1162/coli\\_a\\_00503/2198446/coli\\_a\\_00503.pdf},\n}\n\n\n
\n
\n\n\n
\n  Semantic representations capture the meaning of a text. Abstract Meaning Representation (AMR), a type of semantic representation, focuses on predicate-argument structure and abstracts away from surface form. Though AMR was developed initially for English, it has now been adapted to a multitude of languages in the form of non-English annotation schemas, cross-lingual text-to-AMR parsing, and AMR-to-(non-English) text generation. We advance prior work on cross-lingual AMR by thoroughly investigating the amount, types, and causes of differences which appear in AMRs of different languages. Further, we compare how AMR captures meaning in cross-lingual pairs versus strings, and show that AMR graphs are able to draw out fine-grained differences between parallel sentences. We explore three primary research questions: (1) What are the types and causes of differences in parallel AMRs? (2) How can we measure the amount of difference between AMR pairs in different languages? (3) Given that AMR structure is affected by language and exhibits cross-lingual differences, how do cross-lingual AMR pairs compare to string-based representations of cross-lingual sentence pairs? We find that the source language itself does have a measurable impact on AMR structure, and that translation divergences and annotator choices also lead to differences in cross-lingual AMR pairs. We explore the implications of this finding throughout our study, concluding that, while AMR is useful to capture meaning across languages, evaluations need to take into account source language influences if they are to paint an accurate picture of system output, and meaning generally.\n
\n\n\n
\n\n\n\n\n\n    
\n  
\n\n  
\n    
\n       \n      2023\n      \n        \n        (7)\n        \n      \n    
\n    
\n      \n  \n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Follow the leader(board) with confidence: Estimating p-values from a single test set with item and response variance.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  Christopher Homan;  Lora Aroyo;  and  Chris Welty.\n\n\n  \n\n\n\n  In Findings of the Association for Computational Linguistics: ACL 2023, pages 3138–3161, Toronto, Canada, July 2023. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"FollowPaper\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    4 downloads\n  \n  \n\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@inproceedings{wein-etal-2023-follow,\n    title = "Follow the leader(board) with confidence: Estimating p-values from a single test set with item and response variance",\n    author = "Wein, Shira  and\n      Homan, Christopher  and\n      Aroyo, Lora  and\n      Welty, Chris",\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.196",\n    doi = "10.18653/v1/2023.findings-acl.196",\n    pages = "3138--3161",\n    abstract = "Among the problems with leaderboard culture in NLP has been the widespread lack of confidence estimation in reported results. In this work, we present a framework and simulator for estimating p-values for comparisons between the results of two systems, in order to understand the confidence that one is actually better (i.e. ranked higher) than the other. What has made this difficult in the past is that each system must itself be evaluated by comparison to a gold standard. We define a null hypothesis that each system{'}s metric scores are drawn from the same distribution, using variance found naturally (though rarely reported) in test set items and individual labels on an item (responses) to produce the metric distributions. We create a test set that evenly mixes the responses of the two systems under the assumption the null hypothesis is true. Exploring how to best estimate the true p-value from a single test set under different metrics, tests, and sampling methods, we find that the presence of response variance (from multiple raters or multiple model versions) has a profound impact on p-value estimates for model comparison, and that choice of metric and sampling method is critical to providing statistical guarantees on model comparisons.",\n}\n\n
\n
\n\n\n
\n  Among the problems with leaderboard culture in NLP has been the widespread lack of confidence estimation in reported results. In this work, we present a framework and simulator for estimating p-values for comparisons between the results of two systems, in order to understand the confidence that one is actually better (i.e. ranked higher) than the other. What has made this difficult in the past is that each system must itself be evaluated by comparison to a gold standard. We define a null hypothesis that each system's metric scores are drawn from the same distribution, using variance found naturally (though rarely reported) in test set items and individual labels on an item (responses) to produce the metric distributions. We create a test set that evenly mixes the responses of the two systems under the assumption the null hypothesis is true. Exploring how to best estimate the true p-value from a single test set under different metrics, tests, and sampling methods, we find that the presence of response variance (from multiple raters or multiple model versions) has a profound impact on p-value estimates for model comparison, and that choice of metric and sampling method is critical to providing statistical guarantees on model comparisons.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Human Raters Cannot Distinguish English Translations from Original English Texts.\n  \n  \n  \n  \n\n\n  \n   Shira Wein.\n\n\n  \n\n\n\n  In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12266–12272, Singapore, December 2023. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"HumanPaper\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    4 downloads\n  \n  \n\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@inproceedings{wein-2023-human,\n    title = "Human Raters Cannot Distinguish {E}nglish Translations from Original {E}nglish Texts",\n    author = "Wein, Shira",\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.754",\n    doi = "10.18653/v1/2023.emnlp-main.754",\n    pages = "12266--12272",\n    abstract = "The term translationese describes the set of linguistic features unique to translated texts, which appear regardless of translation quality. Though automatic classifiers designed to distinguish translated texts achieve high accuracy and prior work has identified common hallmarks of translationese, human accuracy of identifying translated text is understudied. In this work, we perform a human evaluation of English original/translated texts in order to explore raters{'} ability to classify texts as being original or translated English and the features that lead a rater to judge text as being translated. Ultimately, we find that, regardless of the annotators{'} native language or the source language of the text, annotators are unable to distinguish translations from original English texts and also have low agreement. Our results provide critical insight into work in translation studies and context for assessments of translationese classifiers.",\n}\n\n
\n
\n\n\n
\n  The term translationese describes the set of linguistic features unique to translated texts, which appear regardless of translation quality. Though automatic classifiers designed to distinguish translated texts achieve high accuracy and prior work has identified common hallmarks of translationese, human accuracy of identifying translated text is understudied. In this work, we perform a human evaluation of English original/translated texts in order to explore raters' ability to classify texts as being original or translated English and the features that lead a rater to judge text as being translated. Ultimately, we find that, regardless of the annotators' native language or the source language of the text, annotators are unable to distinguish translations from original English texts and also have low agreement. Our results provide critical insight into work in translation studies and context for assessments of translationese classifiers.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  Zhuxin Wang;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the 15th International Conference on Computational Semantics, pages 144–154, Nancy, France, June 2023. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"MeasuringPaper\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    1 download\n  \n  \n\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@inproceedings{wein-etal-2023-measuring,\n    title = "Measuring Fine-Grained Semantic Equivalence with {A}bstract {M}eaning {R}epresentation",\n    author = "Wein, Shira  and\n      Wang, Zhuxin  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the 15th International Conference on Computational Semantics",\n    month = jun,\n    year = "2023",\n    address = "Nancy, France",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.iwcs-1.16",\n    pages = "144--154",\n    abstract = "Identifying semantically equivalent sentences is important for many NLP tasks. Current approaches to semantic equivalence take a loose, sentence-level approach to {``}equivalence,{''} despite evidence that fine-grained differences and implicit content have an effect on human understanding and system performance. In this work, we introduce a novel, more sensitive method of characterizing cross-lingual semantic equivalence that leverages Abstract Meaning Representation graph structures. We find that parsing sentences into AMRs and comparing the AMR graphs enables finer-grained equivalence measurement than comparing the sentences themselves. We demonstrate that when using gold or even automatically parsed AMR annotations, our solution is finer-grained than existing corpus filtering methods and more accurate at predicting strictly equivalent sentences than existing semantic similarity metrics.",\n}\n\n
\n
\n\n\n
\n  Identifying semantically equivalent sentences is important for many NLP tasks. Current approaches to semantic equivalence take a loose, sentence-level approach to ``equivalence,'' despite evidence that fine-grained differences and implicit content have an effect on human understanding and system performance. In this work, we introduce a novel, more sensitive method of characterizing cross-lingual semantic equivalence that leverages Abstract Meaning Representation graph structures. We find that parsing sentences into AMRs and comparing the AMR graphs enables finer-grained equivalence measurement than comparing the sentences themselves. We demonstrate that when using gold or even automatically parsed AMR annotations, our solution is finer-grained than existing corpus filtering methods and more accurate at predicting strictly equivalent sentences than existing semantic similarity metrics.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    AMR4NLI: Interpretable and robust NLI measures from semantic graphs.\n  \n  \n  \n  \n\n\n  \n   Juri Opitz;  Shira Wein;  Julius Steen;  Anette Frank;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the 15th International Conference on Computational Semantics, pages 275–283, Nancy, France, June 2023. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"AMR4NLI: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    1 download\n  \n  \n\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@inproceedings{opitz-etal-2023-amr4nli,\n    title = "{AMR}4{NLI}: Interpretable and robust {NLI} measures from semantic graphs",\n    author = "Opitz, Juri  and\n      Wein, Shira  and\n      Steen, Julius  and\n      Frank, Anette  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the 15th International Conference on Computational Semantics",\n    month = jun,\n    year = "2023",\n    address = "Nancy, France",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.iwcs-1.29",\n    pages = "275--283",\n    abstract = "The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but the meaning relationships driving these ratings are not formalized. Can the underlying sentence pair relationships be made more explicit in an interpretable yet robust fashion? We compare semantic structures to represent premise and hypothesis, including sets of *contextualized embeddings* and *semantic graphs* (Abstract Meaning Representations), and measure whether the hypothesis is a semantic substructure of the premise, utilizing interpretable metrics. Our evaluation on three English benchmarks finds value in both contextualized embeddings and semantic graphs; moreover, they provide complementary signals, and can be leveraged together in a hybrid model.",\n}\n\n
\n
\n\n\n
\n  The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but the meaning relationships driving these ratings are not formalized. Can the underlying sentence pair relationships be made more explicit in an interpretable yet robust fashion? We compare semantic structures to represent premise and hypothesis, including sets of *contextualized embeddings* and *semantic graphs* (Abstract Meaning Representations), and measure whether the hypothesis is a semantic substructure of the premise, utilizing interpretable metrics. Our evaluation on three English benchmarks finds value in both contextualized embeddings and semantic graphs; moreover, they provide complementary signals, and can be leveraged together in a hybrid model.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Comparing UMR and Cross-lingual Adaptations of AMR.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  and  Julia Bonn.\n\n\n  \n\n\n\n  In Proceedings of the Fourth International Workshop on Designing Meaning Representations, pages 23–33, Nancy, France, June 2023. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"ComparingPaper\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    1 download\n  \n  \n\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@inproceedings{wein-bonn-2023-comparing,\n    title = "Comparing {UMR} and Cross-lingual Adaptations of {AMR}",\n    author = "Wein, Shira  and\n      Bonn, Julia",\n    booktitle = "Proceedings of the Fourth International Workshop on Designing Meaning Representations",\n    month = jun,\n    year = "2023",\n    address = "Nancy, France",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.dmr-1.3",\n    pages = "23--33",\n    abstract = "Abstract Meaning Representation (AMR) is a popular semantic annotation schema that presents sentence meaning as a graph while abstracting away from syntax. It was originally designed for English, but has since been extended to a variety of non-English versions of AMR. These cross-lingual adaptations, to varying degrees, incorporate language-specific features necessary to effectively capture the semantics of the language being annotated. Uniform Meaning Representation (UMR) on the other hand, the multilingual extension of AMR, was designed specifically for cross-lingual applications. In this work, we discuss these two approaches to extending AMR beyond English. We describe both approaches, compare the information they capture for a case language (Spanish), and outline implications for future work.",\n}\n\n
\n
\n\n\n
\n  Abstract Meaning Representation (AMR) is a popular semantic annotation schema that presents sentence meaning as a graph while abstracting away from syntax. It was originally designed for English, but has since been extended to a variety of non-English versions of AMR. These cross-lingual adaptations, to varying degrees, incorporate language-specific features necessary to effectively capture the semantics of the language being annotated. Uniform Meaning Representation (UMR) on the other hand, the multilingual extension of AMR, was designed specifically for cross-lingual applications. In this work, we discuss these two approaches to extending AMR beyond English. We describe both approaches, compare the information they capture for a case language (Spanish), and outline implications for future work.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    UMR Annotation of Multiword Expressions.\n  \n  \n  \n  \n\n\n  \n   Julia Bonn;  Andrew Cowell;  Jan Hajič;  Alexis Palmer;  Martha Palmer;  James Pustejovsky;  Haibo Sun;  Zdenka Uresova;  Shira Wein;  Nianwen Xue;  and  Jin Zhao.\n\n\n  \n\n\n\n  In Proceedings of the Fourth International Workshop on Designing Meaning Representations, pages 99–109, Nancy, France, June 2023. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"UMRPaper\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{bonn-etal-2023-umr,\n    title = "{UMR} Annotation of Multiword Expressions",\n    author = "Bonn, Julia  and\n      Cowell, Andrew  and\n      Haji{\\v{c}}, Jan  and\n      Palmer, Alexis  and\n      Palmer, Martha  and\n      Pustejovsky, James  and\n      Sun, Haibo  and\n      Uresova, Zdenka  and\n      Wein, Shira  and\n      Xue, Nianwen  and\n      Zhao, Jin",\n    booktitle = "Proceedings of the Fourth International Workshop on Designing Meaning Representations",\n    month = jun,\n    year = "2023",\n    address = "Nancy, France",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2023.dmr-1.10",\n    pages = "99--109",\n    abstract = "Rooted in AMR, Uniform Meaning Representation (UMR) is a graph-based formalism with nodes as concepts and edges as relations between them. When used to represent natural language semantics, UMR maps words in a sentence to concepts in the UMR graph. Multiword expressions (MWEs) pose a particular challenge to UMR annotation because they deviate from the default one-to-one mapping between words and concepts. There are different types of MWEs which require different kinds of annotation that must be specified in guidelines. This paper discusses the specific treatment for each type of MWE in UMR.",\n}\n\n
\n
\n\n\n
\n  Rooted in AMR, Uniform Meaning Representation (UMR) is a graph-based formalism with nodes as concepts and edges as relations between them. When used to represent natural language semantics, UMR maps words in a sentence to concepts in the UMR graph. Multiword expressions (MWEs) pose a particular challenge to UMR annotation because they deviate from the default one-to-one mapping between words and concepts. There are different types of MWEs which require different kinds of annotation that must be specified in guidelines. This paper discusses the specific treatment for each type of MWE in UMR.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    How Many Raters Do You Need? Power Analysis for Foundation Models.\n  \n  \n  \n  \n\n\n  \n   Christopher M Homan;  Shira Wein;  Lora M Aroyo;  and  Chris Welty.\n\n\n  \n\n\n\n  In Proceedings of I Can't Believe It's Not Better (ICBINB): Failure Modes in the Age of Foundation Models,  2023. NeurIPS\n  \n\n\n\n
\n\n\n\n  \n  \n    \"HowPaper\n   \n  \n\n  \n\n  \n  link\n   \n  \n\n  bibtex\n    \n\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{homanmany,\n  title={How Many Raters Do You Need? Power Analysis for Foundation Models},\n  author={Homan, Christopher M and Wein, Shira and Aroyo, Lora M and Welty, Chris},\n  year={2023},\n  url="https://neurips.cc/virtual/2023/76515#details",\n  booktitle="Proceedings of I Can't Believe It's Not Better (ICBINB): Failure Modes in the Age of Foundation Models",\n  publisher = "NeurIPS"\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n    
\n  
\n\n  
\n    
\n       \n      2022\n      \n        \n        (7)\n        \n      \n    
\n    
\n      \n  \n  
\n  \n\n  \n  \n  \n  \n  \n  \n    A CS1 Open Data Analysis Project with Embedded Ethics.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  Alicia Patterson;  Shannon Brick;  and  Sydney Luken.\n\n\n  \n\n\n\n  11 2022.\n  \n\n\n\n
\n\n\n\n  \n  \n    \"APaper\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  
@book{wein2023cs1,\nauthor = {Wein, Shira and Patterson, Alicia and Brick, Shannon and Luken, Sydney},\nyear = {2022},\nmonth = {11},\ntitle = {A CS1 Open Data Analysis Project with Embedded Ethics},\nisbn = {9798400704680},\ndoi = {10.1145/3631987},\nbooktile="ACM EngageCSEdu",\nurl="https://dl.acm.org/doi/pdf/10.1145/3631987"\n}\n\n
\n
\n\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Spanish Abstract Meaning Representation: Annotation of a General Corpus.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  Lucia Donatelli;  Ethan Ricker;  Calvin Engstrom;  Alex Nelson;  Leonie Harter;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Northern European Journal of Language Technology, Volume 8, Copenhagen, Denmark,  2022. Northern European Association of Language Technology\n  \n\n\n\n
\n\n\n\n  \n  \n    \"SpanishPaper\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{wein-2022-spanish,\n    title = "{S}panish {A}bstract {M}eaning {R}epresentation: Annotation of a General Corpus",\n    author = "Wein, Shira  and\n      Donatelli, Lucia  and\n      Ricker, Ethan  and\n      Engstrom, Calvin  and\n      Nelson, Alex  and\n      Harter, Leonie  and\n      Schneider, Nathan",\n    booktitle = "Northern European Journal of Language Technology, Volume 8",\n    year = "2022",\n    address = "Copenhagen, Denmark",\n    publisher = "Northern European Association of Language Technology",\n    url = "https://aclanthology.org/2022.nejlt-1.6",\n    doi = "https://doi.org/10.3384/nejlt.2000-1533.2022.4462",\n    abstract = "Abstract Meaning Representation (AMR), originally designed for English, has been adapted to a number of languages to facilitate cross-lingual semantic representation and analysis. We build on previous work and present the first sizable, general annotation project for Spanish AMR. We release a detailed set of annotation guidelines and a corpus of 486 gold-annotated sentences spanning multiple genres from an existing, cross-lingual AMR corpus. Our work constitutes the second largest non-English gold AMR corpus to date. Fine-tuning an AMR to-Spanish generation model with our annotations results in a BERTScore improvement of 8.8{\\%}, demonstrating initial utility of our work.",\n}\n\n
\n
\n\n\n
\n  Abstract Meaning Representation (AMR), originally designed for English, has been adapted to a number of languages to facilitate cross-lingual semantic representation and analysis. We build on previous work and present the first sizable, general annotation project for Spanish AMR. We release a detailed set of annotation guidelines and a corpus of 486 gold-annotated sentences spanning multiple genres from an existing, cross-lingual AMR corpus. Our work constitutes the second largest non-English gold AMR corpus to date. Fine-tuning an AMR to-Spanish generation model with our annotations results in a BERTScore improvement of 8.8%, demonstrating initial utility of our work.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Accounting for Language Effect in the Evaluation of Cross-lingual AMR Parsers.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the 29th International Conference on Computational Linguistics, pages 3824–3834, Gyeongju, Republic of Korea, October 2022. International Committee on Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"AccountingPaper\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    1 download\n  \n  \n\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@inproceedings{wein-schneider-2022-accounting,\n    title = "Accounting for Language Effect in the Evaluation of Cross-lingual {AMR} Parsers",\n    author = "Wein, Shira  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",\n    month = oct,\n    year = "2022",\n    address = "Gyeongju, Republic of Korea",\n    publisher = "International Committee on Computational Linguistics",\n    url = "https://aclanthology.org/2022.coling-1.336",\n    pages = "3824--3834",\n    abstract = "Cross-lingual Abstract Meaning Representation (AMR) parsers are currently evaluated in comparison to gold English AMRs, despite parsing a language other than English, due to the lack of multilingual AMR evaluation metrics. This evaluation practice is problematic because of the established effect of source language on AMR structure. In this work, we present three multilingual adaptations of monolingual AMR evaluation metrics and compare the performance of these metrics to sentence-level human judgments. We then use our most highly correlated metric to evaluate the output of state-of-the-art cross-lingual AMR parsers, finding that Smatch may still be a useful metric in comparison to gold English AMRs, while our multilingual adaptation of S2match (XS2match) is best for comparison with gold in-language AMRs.",\n}\n\n
\n
\n\n\n
\n  Cross-lingual Abstract Meaning Representation (AMR) parsers are currently evaluated in comparison to gold English AMRs, despite parsing a language other than English, due to the lack of multilingual AMR evaluation metrics. This evaluation practice is problematic because of the established effect of source language on AMR structure. In this work, we present three multilingual adaptations of monolingual AMR evaluation metrics and compare the performance of these metrics to sentence-level human judgments. We then use our most highly correlated metric to evaluate the output of state-of-the-art cross-lingual AMR parsers, finding that Smatch may still be a useful metric in comparison to gold English AMRs, while our multilingual adaptation of S2match (XS2match) is best for comparison with gold in-language AMRs.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Crowdsourcing Preposition Sense Disambiguation with High Precision via a Priming Task.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances), pages 15–22, Abu Dhabi, United Arab Emirates (Hybrid), December 2022. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"CrowdsourcingPaper\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{wein-schneider-2022-crowdsourcing,\n    title = "Crowdsourcing Preposition Sense Disambiguation with High Precision via a Priming Task",\n    author = "Wein, Shira  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the Fourth Workshop on Data Science with Human-in-the-Loop (Language Advances)",\n    month = dec,\n    year = "2022",\n    address = "Abu Dhabi, United Arab Emirates (Hybrid)",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2022.dash-1.3",\n    pages = "15--22",\n    abstract = "The careful design of a crowdsourcing protocol is critical to eliciting highly accurate annotations from untrained workers. In this work, we explore the development of crowdsourcing protocols for a challenging word sense disambiguation task. We find that (a) selecting a similar example usage can serve as a proxy for selecting an explicit definition of the sense, and (b) priming workers with an additional, related task within the HIT improves performance on the main proxy task. Ultimately, we demonstrate the usefulness of our crowdsourcing elicitation technique as an effective alternative to previously investigated training strategies, which can be used if agreement on a challenging task is low.",\n}\n\n
\n
\n\n\n
\n  The careful design of a crowdsourcing protocol is critical to eliciting highly accurate annotations from untrained workers. In this work, we explore the development of crowdsourcing protocols for a challenging word sense disambiguation task. We find that (a) selecting a similar example usage can serve as a proxy for selecting an explicit definition of the sense, and (b) priming workers with an additional, related task within the HIT improves performance on the main proxy task. Ultimately, we demonstrate the usefulness of our crowdsourcing elicitation technique as an effective alternative to previously investigated training strategies, which can be used if agreement on a challenging task is low.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Semantic Similarity as a Window into Vector- and Graph-Based Metrics.\n  \n  \n  \n  \n\n\n  \n   Wai Ching Leung;  Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 106–115, Abu Dhabi, United Arab Emirates (Hybrid), December 2022. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"SemanticPaper\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{leung-etal-2022-semantic,\n    title = "Semantic Similarity as a Window into Vector- and Graph-Based Metrics",\n    author = "Leung, Wai Ching  and\n      Wein, Shira  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",\n    month = dec,\n    year = "2022",\n    address = "Abu Dhabi, United Arab Emirates (Hybrid)",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2022.gem-1.8",\n    doi = "10.18653/v1/2022.gem-1.8",\n    pages = "106--115",\n    abstract = "In this work, we use sentence similarity as a lens through which to investigate the representation of meaning in graphs vs. vectors. On semantic textual similarity data, we examine how similarity metrics based on vectors alone (SENTENCE-BERT and BERTSCORE) fare compared to metrics based on AMR graphs (SMATCH and S2MATCH). Quantitative and qualitative analyses show that the AMR-based metrics can better capture meanings dependent on sentence structures, but can also be distracted by structural differences{---}whereas the BERT-based metrics represent finer-grained meanings of individual words, but often fail to capture the ordering effect of words within sentences and suffer from interpretability problems. These findings contribute to our understanding of each approach to semantic representation and motivate distinct use cases for graph and vector-based representations.",\n}\n\n
\n
\n\n\n
\n  In this work, we use sentence similarity as a lens through which to investigate the representation of meaning in graphs vs. vectors. On semantic textual similarity data, we examine how similarity metrics based on vectors alone (SENTENCE-BERT and BERTSCORE) fare compared to metrics based on AMR graphs (SMATCH and S2MATCH). Quantitative and qualitative analyses show that the AMR-based metrics can better capture meanings dependent on sentence structures, but can also be distracted by structural differences—whereas the BERT-based metrics represent finer-grained meanings of individual words, but often fail to capture the ordering effect of words within sentences and suffer from interpretability problems. These findings contribute to our understanding of each approach to semantic representation and motivate distinct use cases for graph and vector-based representations.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Effect of Source Language on AMR Structure.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  Wai Ching Leung;  Yifu Mu;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022, pages 97–102, Marseille, France, June 2022. European Language Resources Association\n  \n\n\n\n
\n\n\n\n  \n  \n    \"EffectPaper\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{wein-etal-2022-effect,\n    title = "Effect of Source Language on {AMR} Structure",\n    author = "Wein, Shira  and\n      Leung, Wai Ching  and\n      Mu, Yifu  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022",\n    month = jun,\n    year = "2022",\n    address = "Marseille, France",\n    publisher = "European Language Resources Association",\n    url = "https://aclanthology.org/2022.law-1.12",\n    pages = "97--102",\n    abstract = "The Abstract Meaning Representation (AMR) annotation schema was originally designed for English. But the formalism has since been adapted for annotation in a variety of languages. Meanwhile, cross-lingual parsers have been developed to derive English AMR representations for sentences from other languages{---}implicitly assuming that English AMR can approximate an interlingua. In this work, we investigate the similarity of AMR annotations in parallel data and how much the language matters in terms of the graph structure. We set out to quantify the effect of sentence language on the structure of the parsed AMR. As a case study, we take parallel AMR annotations from Mandarin Chinese and English AMRs, and replace all Chinese concepts with equivalent English tokens. We then compare the two graphs via the Smatch metric as a measure of structural similarity. We find that source language has a dramatic impact on AMR structure, with Smatch scores below 50{\\%} between English and Chinese graphs in our sample{---}an important reference point for interpreting Smatch scores in cross-lingual AMR parsing.",\n}\n\n
\n
\n\n\n
\n  The Abstract Meaning Representation (AMR) annotation schema was originally designed for English. But the formalism has since been adapted for annotation in a variety of languages. Meanwhile, cross-lingual parsers have been developed to derive English AMR representations for sentences from other languages—implicitly assuming that English AMR can approximate an interlingua. In this work, we investigate the similarity of AMR annotations in parallel data and how much the language matters in terms of the graph structure. We set out to quantify the effect of sentence language on the structure of the parsed AMR. As a case study, we take parallel AMR annotations from Mandarin Chinese and English AMRs, and replace all Chinese concepts with equivalent English tokens. We then compare the two graphs via the Smatch metric as a measure of structural similarity. We find that source language has a dramatic impact on AMR structure, with Smatch scores below 50% between English and Chinese graphs in our sample—an important reference point for interpreting Smatch scores in cross-lingual AMR parsing.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Can adult lexical diversity be measured bilingually? A proof-of-concept study.\n  \n  \n  \n  \n\n\n  \n   Rima Elabdali;  Shira Wein;  and  Lourdes Ortega.\n\n\n  \n\n\n\n  Bilingual Writers and Corpus Analysis,121.  2022.\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\n  \n  \n  \n  \n  \n  \n  \n\n   \n  \n  \n\n\n\n
\n  
@article{elabdali2022can,\n  title={Can adult lexical diversity be measured bilingually? A proof-of-concept study},\n  author={Elabdali, Rima and Wein, Shira and Ortega, Lourdes},\n  journal={Bilingual Writers and Corpus Analysis},\n  pages={121},\n  year={2022},\n  publisher={Taylor \\& Francis},\n  url="https://www.taylorfrancis.com/chapters/edit/10.4324/9781003183921-5/adult-lexical-diversity-measured-bilingually-proof-concept-study-rima-elabdali-shira-wein-lourdes-ortega?context=ubx&refId=baf5ad86-dc0f-4cb5-8b69-468c6233988d"\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n    
\n  
\n\n  
\n    
\n       \n      2021\n      \n        \n        (3)\n        \n      \n    
\n    
\n      \n  \n  
\n  \n\n  \n  \n  \n  \n  \n  \n    A Fully Automated Approach to Requirement Extraction from Design Documents.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  and  Paul Briggs.\n\n\n  \n\n\n\n  In 2021 IEEE Aerospace Conference (50100), pages 1-7,  2021. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"APaper\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  
@INPROCEEDINGS{9438170,\n  author={Wein, Shira and Briggs, Paul},\n  booktitle={2021 IEEE Aerospace Conference (50100)}, \n  title={A Fully Automated Approach to Requirement Extraction from Design Documents}, \n  year={2021},\n  volume={},\n  number={},\n  pages={1-7},\n  keywords={Measurement;Databases;Manuals;Writing;Feature extraction;Software;Natural language processing},\n  doi={10.1109/AERO50100.2021.9438170},\n  url="https://ieeexplore.ieee.org/abstract/document/9438170"\n}\n\n
\n
\n\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Classifying Divergences in Cross-lingual AMR Pairs.\n  \n  \n  \n  \n\n\n  \n   Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop, pages 56–65, Punta Cana, Dominican Republic, November 2021. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"ClassifyingPaper\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{wein-schneider-2021-classifying,\n    title = "Classifying Divergences in Cross-lingual {AMR} Pairs",\n    author = "Wein, Shira  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop",\n    month = nov,\n    year = "2021",\n    address = "Punta Cana, Dominican Republic",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2021.law-1.6",\n    doi = "10.18653/v1/2021.law-1.6",\n    pages = "56--65",\n    abstract = "Translation divergences are varied and widespread, challenging approaches that rely on parallel text. To annotate translation divergences, we propose a schema grounded in the Abstract Meaning Representation (AMR), a sentence-level semantic framework instantiated for a number of languages. By comparing parallel AMR graphs, we can identify specific points of divergence. Each divergence is labeled with both a type and a cause. We release a small corpus of annotated English-Spanish data, and analyze the annotations in our corpus.",\n}\n\n
\n
\n\n\n
\n  Translation divergences are varied and widespread, challenging approaches that rely on parallel text. To annotate translation divergences, we propose a schema grounded in the Abstract Meaning Representation (AMR), a sentence-level semantic framework instantiated for a number of languages. By comparing parallel AMR graphs, we can identify specific points of divergence. Each divergence is labeled with both a type and a cause. We release a small corpus of annotated English-Spanish data, and analyze the annotations in our corpus.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Supersense and Sensibility: Proxy Tasks for Semantic Annotation of Prepositions.\n  \n  \n  \n  \n\n\n  \n   Luke Gessler;  Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the Society for Computation in Linguistics 2021, pages 464–466, Online, February 2021. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"SupersensePaper\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{gessler-etal-2021-supersense,\n    title = "Supersense and Sensibility: Proxy Tasks for Semantic Annotation of Prepositions",\n    author = "Gessler, Luke  and\n      Wein, Shira  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the Society for Computation in Linguistics 2021",\n    month = feb,\n    year = "2021",\n    address = "Online",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2021.scil-1.58",\n    pages = "464--466",\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n    
\n  
\n\n  
\n    
\n       \n      2020\n      \n        \n        (4)\n        \n      \n    
\n    
\n      \n  \n  
\n  \n\n  \n  \n  \n  \n  \n  \n    A Human Evaluation of AMR-to-English Generation Systems.\n  \n  \n  \n  \n\n\n  \n   Emma Manning;  Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the 28th International Conference on Computational Linguistics, pages 4773–4786, Barcelona, Spain (Online), December 2020. International Committee on Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"APaper\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{manning-etal-2020-human,\n    title = "A Human Evaluation of {AMR}-to-{E}nglish Generation Systems",\n    author = "Manning, Emma  and\n      Wein, Shira  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",\n    month = dec,\n    year = "2020",\n    address = "Barcelona, Spain (Online)",\n    publisher = "International Committee on Computational Linguistics",\n    url = "https://aclanthology.org/2020.coling-main.420",\n    doi = "10.18653/v1/2020.coling-main.420",\n    pages = "4773--4786",\n    abstract = "Most current state-of-the art systems for generating English text from Abstract Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic for natural language generation. In this work, we present the results of a new human evaluation which collects fluency and adequacy scores, as well as categorization of error types, for several recent AMR generation systems. We discuss the relative quality of these systems and how our results compare to those of automatic metrics, finding that while the metrics are mostly successful in ranking systems overall, collecting human judgments allows for more nuanced comparisons. We also analyze common errors made by these systems.",\n}\n\n
\n
\n\n\n
\n  Most current state-of-the art systems for generating English text from Abstract Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic for natural language generation. In this work, we present the results of a new human evaluation which collects fluency and adequacy scores, as well as categorization of error types, for several recent AMR generation systems. We discuss the relative quality of these systems and how our results compare to those of automatic metrics, finding that while the metrics are mostly successful in ranking systems overall, collecting human judgments allows for more nuanced comparisons. We also analyze common errors made by these systems.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    PASTRIE: A Corpus of Prepositions Annotated with Supersense Tags in Reddit International English.\n  \n  \n  \n  \n\n\n  \n   Michael Kranzlein;  Emma Manning;  Siyao Peng;  Shira Wein;  Aryaman Arora;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the 14th Linguistic Annotation Workshop, pages 105–116, Barcelona, Spain, December 2020. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"PASTRIE: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{kranzlein-etal-2020-pastrie,\n    title = "{PASTRIE}: A Corpus of Prepositions Annotated with Supersense Tags in {R}eddit International {E}nglish",\n    author = "Kranzlein, Michael  and\n      Manning, Emma  and\n      Peng, Siyao  and\n      Wein, Shira  and\n      Arora, Aryaman  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",\n    month = dec,\n    year = "2020",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2020.law-1.10",\n    pages = "105--116",\n    abstract = "We present the Prepositions Annotated with Supsersense Tags in Reddit International English ({``}PASTRIE{''}) corpus, a new dataset containing manually annotated preposition supersenses of English data from presumed speakers of four L1s: English, French, German, and Spanish. The annotations are comprehensive, covering all preposition types and tokens in the sample. Along with the corpus, we provide analysis of distributional patterns across the included L1s and a discussion of the influence of L1s on L2 preposition choice.",\n}\n\n
\n
\n\n\n
\n  We present the Prepositions Annotated with Supsersense Tags in Reddit International English (``PASTRIE'') corpus, a new dataset containing manually annotated preposition supersenses of English data from presumed speakers of four L1s: English, French, German, and Spanish. The annotations are comprehensive, covering all preposition types and tokens in the sample. Along with the corpus, we provide analysis of distributional patterns across the included L1s and a discussion of the influence of L1s on L2 preposition choice.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Supersense and Sensibility: Proxy Tasks for Semantic Annotation of Prepositions.\n  \n  \n  \n  \n\n\n  \n   Luke Gessler;  Shira Wein;  and  Nathan Schneider.\n\n\n  \n\n\n\n  In Proceedings of the 14th Linguistic Annotation Workshop, pages 117–126, Barcelona, Spain, December 2020. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"SupersensePaper\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{gessler-etal-2020-supersense,\n    title = "Supersense and Sensibility: Proxy Tasks for Semantic Annotation of Prepositions",\n    author = "Gessler, Luke  and\n      Wein, Shira  and\n      Schneider, Nathan",\n    booktitle = "Proceedings of the 14th Linguistic Annotation Workshop",\n    month = dec,\n    year = "2020",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2020.law-1.11",\n    pages = "117--126",\n    abstract = "Prepositional supersense annotation is time-consuming and requires expert training. Here, we present two sensible methods for obtaining prepositional supersense annotations indirectly by eliciting surface substitution and similarity judgments. Four pilot studies suggest that both methods have potential for producing prepositional supersense annotations that are comparable in quality to expert annotations.",\n}\n\n
\n
\n\n\n
\n  Prepositional supersense annotation is time-consuming and requires expert training. Here, we present two sensible methods for obtaining prepositional supersense annotations indirectly by eliciting surface substitution and similarity judgments. Four pilot studies suggest that both methods have potential for producing prepositional supersense annotations that are comparable in quality to expert annotations.\n
\n\n\n
\n\n\n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Classification and Analysis of Neologisms Produced by Learners of Spanish: Effects of Proficiency and Task.\n  \n  \n  \n  \n\n\n  \n   Shira Wein.\n\n\n  \n\n\n\n  In Proceedings of the The Fourth Widening Natural Language Processing Workshop, pages 88–91, Seattle, USA, July 2020. Association for Computational Linguistics\n  \n\n\n\n
\n\n\n\n  \n  \n    \"ClassificationPaper\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{wein-2020-classification,\n    title = "Classification and Analysis of Neologisms Produced by Learners of {S}panish: Effects of Proficiency and Task",\n    author = "Wein, Shira",\n    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",\n    month = jul,\n    year = "2020",\n    address = "Seattle, USA",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2020.winlp-1.22",\n    doi = "10.18653/v1/2020.winlp-1.22",\n    pages = "88--91",\n    abstract = "The Spanish Learner Language Oral Corpora (SPLLOC) of transcribed conversations between investigators and language learners contains a set of neologism tags. In this work, the utterances tagged as neologisms are broken down into three categories: true neologisms, loanwords, and errors. This work examines the relationships between neologism, loanword, and error production and both language learner level and conversation task. The results of this study suggest that loanwords and errors are produced most frequently by language learners with moderate experience, while neologisms are produced most frequently by native speakers. This study also indicates that tasks that require descriptions of images draw more neologism, loanword and error production. We ultimately present a unique analysis of the implications of neologism, loanword, and error production useful for further work in second language acquisition research, as well as for language educators.",\n}\n
\n
\n\n\n
\n  The Spanish Learner Language Oral Corpora (SPLLOC) of transcribed conversations between investigators and language learners contains a set of neologism tags. In this work, the utterances tagged as neologisms are broken down into three categories: true neologisms, loanwords, and errors. This work examines the relationships between neologism, loanword, and error production and both language learner level and conversation task. The results of this study suggest that loanwords and errors are produced most frequently by language learners with moderate experience, while neologisms are produced most frequently by native speakers. This study also indicates that tasks that require descriptions of images draw more neologism, loanword and error production. We ultimately present a unique analysis of the implications of neologism, loanword, and error production useful for further work in second language acquisition research, as well as for language educators.\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);