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\n  \n 2025\n \n \n (8)\n \n \n
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\n \n\n \n \n \n \n \n \n Meaning Beyond Truth Conditions: Evaluating Discourse Level Understanding via Anaphora Accessibility.\n \n \n \n \n\n\n \n Zhu, X.; Zhou, Z.; Charlow, S.; and Frank, R.\n\n\n \n\n\n\n In Che, W.; Nabende, J.; Shutova, E.; and Pilehvar, M. T., editor(s), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8824–8842, Vienna, Austria, July 2025. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"MeaningPaper\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{zhu-etal-2025-meaning,\n    title = "Meaning Beyond Truth Conditions: Evaluating Discourse Level Understanding via Anaphora Accessibility",\n    author = "Zhu, Xiaomeng  and\n      Zhou, Zhenghao  and\n      Charlow, Simon  and\n      Frank, Robert",\n    editor = "Che, Wanxiang  and\n      Nabende, Joyce  and\n      Shutova, Ekaterina  and\n      Pilehvar, Mohammad Taher",\n    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = jul,\n    year = "2025",\n    address = "Vienna, Austria",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2025.acl-long.432/",\n    doi = "10.18653/v1/2025.acl-long.432",\n    pages = "8824--8842",\n    ISBN = "979-8-89176-251-0",\n    abstract = "We present a hierarchy of natural language understanding abilities and argue for the importance of moving beyond assessments of understanding at the lexical and sentence levels to the discourse level. We propose the task of anaphora accessibility as a diagnostic for assessing discourse understanding, and to this end, present an evaluation dataset inspired by theoretical research in dynamic semantics. We evaluate human and LLM performance on our dataset and find that LLMs and humans align on some tasks and diverge on others. Such divergence can be explained by LLMs' reliance on specific lexical items during language comprehension, in contrast to human sensitivity to structural abstractions."\n}\n
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\n We present a hierarchy of natural language understanding abilities and argue for the importance of moving beyond assessments of understanding at the lexical and sentence levels to the discourse level. We propose the task of anaphora accessibility as a diagnostic for assessing discourse understanding, and to this end, present an evaluation dataset inspired by theoretical research in dynamic semantics. We evaluate human and LLM performance on our dataset and find that LLMs and humans align on some tasks and diverge on others. Such divergence can be explained by LLMs' reliance on specific lexical items during language comprehension, in contrast to human sensitivity to structural abstractions.\n
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\n \n\n \n \n \n \n \n \n XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs.\n \n \n \n \n\n\n \n He, L.; Nie, E.; Dindar, S. S.; Firoozi, A.; Nguyen, V.; Puffay, C.; Shimizu, R.; Ye, H.; Brennan, J.; Schmid, H.; Schütze, H.; and Mesgarani, N.\n\n\n \n\n\n\n In Hahn, M.; Rani, P.; Kumar, R.; Shcherbakov, A.; Sorokin, A.; Serikov, O.; Cotterell, R.; and Vylomova, E., editor(s), Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, pages 75–81, Vienna, Austria, August 2025. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"XCOMPS: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{he-etal-2025-xcomps,\n    title = "{XCOMPS}: A Multilingual Benchmark of Conceptual Minimal Pairs",\n    author = "He, Linyang  and\n      Nie, Ercong  and\n      Dindar, Sukru Samet  and\n      Firoozi, Arsalan  and\n      Nguyen, Van  and\n      Puffay, Corentin  and\n      Shimizu, Riki  and\n      Ye, Haotian  and\n      Brennan, Jonathan  and\n      Schmid, Helmut  and\n      Schütze, Hinrich  and\n      Mesgarani, Nima",\n    editor = "Hahn, Michael  and\n      Rani, Priya  and\n      Kumar, Ritesh  and\n      Shcherbakov, Andreas  and\n      Sorokin, Alexey  and\n      Serikov, Oleg  and\n      Cotterell, Ryan  and\n      Vylomova, Ekaterina",\n    booktitle = "Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",\n    month = aug,\n    year = "2025",\n    address = "Vienna, Austria",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2025.sigtyp-1.9/",\n    doi = "10.18653/v1/2025.sigtyp-1.9",\n    pages = "75--81",\n    ISBN = "979-8-89176-281-7",\n    abstract = "In this work, we introduce XCOMPS, a multilingual conceptual minimal pair dataset that covers 17 languages.Using this dataset, we evaluate LLMs' multilingual conceptual understanding through metalinguistic prompting, direct probability measurement, and neurolinguistic probing. We find that: 1) LLMs exhibit weaker conceptual understanding for low-resource languages, and accuracy varies across languages despite being tested on the same concept sets. 2) LLMs excel at distinguishing concept-property pairs that are visibly different but exhibit a marked performance drop when negative pairs share subtle semantic similarities. 3) More morphologically complex languages yield lower concept understanding scores and require deeper layers for conceptual reasoning."\n}\n\n
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\n In this work, we introduce XCOMPS, a multilingual conceptual minimal pair dataset that covers 17 languages.Using this dataset, we evaluate LLMs' multilingual conceptual understanding through metalinguistic prompting, direct probability measurement, and neurolinguistic probing. We find that: 1) LLMs exhibit weaker conceptual understanding for low-resource languages, and accuracy varies across languages despite being tested on the same concept sets. 2) LLMs excel at distinguishing concept-property pairs that are visibly different but exhibit a marked performance drop when negative pairs share subtle semantic similarities. 3) More morphologically complex languages yield lower concept understanding scores and require deeper layers for conceptual reasoning.\n
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\n \n\n \n \n \n \n \n \n The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang.\n \n \n \n \n\n\n \n Liu, F.; Chen, Y.; Liu, Y.; Jin, Z.; Tsai, S.; and Zhong, M.\n\n\n \n\n\n\n 2025.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\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{liu2025goldmedalsroomdiagnosing,\n      title={The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang},\n      author={Fenghua Liu and Yulong Chen and Yixuan Liu and Zhujun Jin and Solomon Tsai and Ming Zhong},\n      year={2025},\n      eprint={2509.00425},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      url={https://arxiv.org/abs/2509.00425},\n}\n\n
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\n \n\n \n \n \n \n \n \n LLM Dependency Parsing with In-Context Rules.\n \n \n \n \n\n\n \n Ginn, M.; and Palmer, A.\n\n\n \n\n\n\n In Fei, H.; Tu, K.; Zhang, Y.; Hu, X.; Han, W.; Jia, Z.; Zheng, Z.; Cao, Y.; Zhang, M.; Lu, W.; Siddharth, N.; Øvrelid, L.; Xue, N.; and Zhang, Y., editor(s), Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025), pages 186–196, Vienna, Austria, August 2025. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"LLMPaper\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{ginn-palmer-2025-llm,\n    title = "{LLM} Dependency Parsing with In-Context Rules",\n    author = "Ginn, Michael  and\n      Palmer, Alexis",\n    editor = "Fei, Hao  and\n      Tu, Kewei  and\n      Zhang, Yuhui  and\n      Hu, Xiang  and\n      Han, Wenjuan  and\n      Jia, Zixia  and\n      Zheng, Zilong  and\n      Cao, Yixin  and\n      Zhang, Meishan  and\n      Lu, Wei  and\n      Siddharth, N.  and\n      {\\O}vrelid, Lilja  and\n      Xue, Nianwen  and\n      Zhang, Yue",\n    booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",\n    month = aug,\n    year = "2025",\n    address = "Vienna, Austria",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2025.xllm-1.17/",\n    doi = "10.18653/v1/2025.xllm-1.17",\n    pages = "186--196",\n    ISBN = "979-8-89176-286-2",\n    abstract = "We study whether incorporating rules (in various formats) can aid large language models to perform dependency parsing. We consider a paradigm in which LLMs first produce symbolic rules given fully labeled examples, and the rules are then provided in a subsequent call that performs the actual parsing. In addition, we experiment with providing human-created annotation guidelines in-context to the LLMs. We test on eight low-resource languages from Universal Dependencies, finding that while both methods for rule incorporation improve zero-shot performance, the benefit disappears with a few labeled in-context examples."\n}\n\n
\n
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\n We study whether incorporating rules (in various formats) can aid large language models to perform dependency parsing. We consider a paradigm in which LLMs first produce symbolic rules given fully labeled examples, and the rules are then provided in a subsequent call that performs the actual parsing. In addition, we experiment with providing human-created annotation guidelines in-context to the LLMs. We test on eight low-resource languages from Universal Dependencies, finding that while both methods for rule incorporation improve zero-shot performance, the benefit disappears with a few labeled in-context examples.\n
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\n \n\n \n \n \n \n \n \n How Humans and LLMs Organize Conceptual Knowledge: Exploring Subordinate Categories in Italian.\n \n \n \n \n\n\n \n Pedrotti, A.; Rambelli, G.; Villani, C.; and Bolognesi, M.\n\n\n \n\n\n\n In Che, W.; Nabende, J.; Shutova, E.; and Pilehvar, M. T., editor(s), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4464–4482, Vienna, Austria, July 2025. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"HowPaper\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{pedrotti-etal-2025-humans,\n    title = "How Humans and {LLM}s Organize Conceptual Knowledge: Exploring Subordinate Categories in {I}talian",\n    author = "Pedrotti, Andrea  and\n      Rambelli, Giulia  and\n      Villani, Caterina  and\n      Bolognesi, Marianna",\n    editor = "Che, Wanxiang  and\n      Nabende, Joyce  and\n      Shutova, Ekaterina  and\n      Pilehvar, Mohammad Taher",\n    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = jul,\n    year = "2025",\n    address = "Vienna, Austria",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2025.acl-long.224/",\n    doi = "10.18653/v1/2025.acl-long.224",\n    pages = "4464--4482",\n    ISBN = "979-8-89176-251-0",\n    abstract = "People can categorize the same entity at multiple taxonomic levels, such as basic (bear), superordinate (animal), and subordinate (grizzly bear). While prior research has focused on basic-level categories, this study is the first attempt to examine the organization of categories by analyzing exemplars produced at the subordinate level. We present a new Italian psycholinguistic dataset of human-generated exemplars for 187 concrete words. We then leverage these data to evaluate whether textual and vision LLMs produce meaningful exemplars that align with human category organization across three key tasks: exemplar generation, category induction, and typicality judgment. Our findings show a low alignment between humans and LLMs, consistent with previous studies. However, their performance varies notably across different semantic domains. Ultimately, this study highlights both the promises and the constraints of using AI-generated exemplars to support psychological and linguistic research."\n}\n\n
\n
\n\n\n
\n People can categorize the same entity at multiple taxonomic levels, such as basic (bear), superordinate (animal), and subordinate (grizzly bear). While prior research has focused on basic-level categories, this study is the first attempt to examine the organization of categories by analyzing exemplars produced at the subordinate level. We present a new Italian psycholinguistic dataset of human-generated exemplars for 187 concrete words. We then leverage these data to evaluate whether textual and vision LLMs produce meaningful exemplars that align with human category organization across three key tasks: exemplar generation, category induction, and typicality judgment. Our findings show a low alignment between humans and LLMs, consistent with previous studies. However, their performance varies notably across different semantic domains. Ultimately, this study highlights both the promises and the constraints of using AI-generated exemplars to support psychological and linguistic research.\n
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\n \n\n \n \n \n \n \n \n Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books.\n \n \n \n \n\n\n \n Zhang, C.; Lin, J.; Liu, X.; Zhang, Z.; and Feng, Y.\n\n\n \n\n\n\n In Che, W.; Nabende, J.; Shutova, E.; and Pilehvar, M. T., editor(s), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3977–3997, Vienna, Austria, July 2025. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ReadPaper\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{zhang-etal-2025-read,\n    title = "Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books",\n    author = "Zhang, Chen  and\n      Lin, Jiuheng  and\n      Liu, Xiao  and\n      Zhang, Zekai  and\n      Feng, Yansong",\n    editor = "Che, Wanxiang  and\n      Nabende, Joyce  and\n      Shutova, Ekaterina  and\n      Pilehvar, Mohammad Taher",\n    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = jul,\n    year = "2025",\n    address = "Vienna, Austria",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2025.acl-long.202/",\n    doi = "10.18653/v1/2025.acl-long.202",\n    pages = "3977--3997",\n    ISBN = "979-8-89176-251-0",\n    abstract = "While large language models (LLMs) have shown promise in translating extremely low-resource languages using resources like dictionaries, the effectiveness of grammar books remains debated. This paper investigates the role of grammar books in translating extremely low-resource languages by decomposing it into two key steps: grammar rule retrieval and application. To facilitate the study, we introduce ZhuangRules, a modularized dataset of grammar rules and their corresponding test sentences. Our analysis reveals that rule retrieval constitutes a primary bottleneck in grammar-based translation. Moreover, although LLMs can apply simple rules for translation when explicitly provided, they encounter difficulties in handling more complex rules. To address these challenges, we propose representing grammar rules as code functions, considering their similarities in structure and the benefit of code in facilitating LLM reasoning. Our experiments show that using code rules significantly boosts both rule retrieval and application, ultimately resulting in a 13.1{\\%} BLEU improvement in translation."\n}\n\n
\n
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\n While large language models (LLMs) have shown promise in translating extremely low-resource languages using resources like dictionaries, the effectiveness of grammar books remains debated. This paper investigates the role of grammar books in translating extremely low-resource languages by decomposing it into two key steps: grammar rule retrieval and application. To facilitate the study, we introduce ZhuangRules, a modularized dataset of grammar rules and their corresponding test sentences. Our analysis reveals that rule retrieval constitutes a primary bottleneck in grammar-based translation. Moreover, although LLMs can apply simple rules for translation when explicitly provided, they encounter difficulties in handling more complex rules. To address these challenges, we propose representing grammar rules as code functions, considering their similarities in structure and the benefit of code in facilitating LLM reasoning. Our experiments show that using code rules significantly boosts both rule retrieval and application, ultimately resulting in a 13.1% BLEU improvement in translation.\n
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\n \n\n \n \n \n \n \n \n Linguistic Blind Spots of Large Language Models.\n \n \n \n \n\n\n \n Cheng, J.; and Amiri, H.\n\n\n \n\n\n\n In Kuribayashi, T.; Rambelli, G.; Takmaz, E.; Wicke, P.; Li, J.; and Oh, B., editor(s), Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 1–17, Albuquerque, New Mexico, USA, May 2025. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"LinguisticPaper\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{cheng-amiri-2025-linguistic,\n    title = "Linguistic Blind Spots of Large Language Models",\n    author = "Cheng, Jiali  and\n      Amiri, Hadi",\n    editor = "Kuribayashi, Tatsuki  and\n      Rambelli, Giulia  and\n      Takmaz, Ece  and\n      Wicke, Philipp  and\n      Li, Jixing  and\n      Oh, Byung-Doh",\n    booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",\n    month = may,\n    year = "2025",\n    address = "Albuquerque, New Mexico, USA",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2025.cmcl-1.3/",\n    doi = "10.18653/v1/2025.cmcl-1.3",\n    pages = "1--17",\n    ISBN = "979-8-89176-227-5",\n    abstract = "Large language models (LLMs) serve as the foundation of numerous AI applications today. However, despite their remarkable proficiency in generating coherent text, questions linger regarding their ability in performing fine-grained linguistic annotation tasks, such as detecting nouns or verbs, or identifying more complex syntactic structures like clauses or T-units in input texts. These tasks require precise syntactic and semantic understanding of input text, and when LLMs underperform on specific linguistic structures, it raises concerns about their reliability for detailed linguistic analysis and whether their (even correct) outputs truly reflect an understanding of the inputs. In this paper, we empirically study recent LLMs performance across fine-grained linguistic annotation tasks. Through a series of experiments, we find that recent LLMs show limited efficacy in addressing linguistic queries and often struggle with linguistically complex inputs. We show that the most capable LLM (Llama3-70b) makes notable errors in detecting linguistic structures, such as misidentifying embedded clauses, failing to recognize verb phrases, and confusing complex nominals with clauses. Our study provides valuable insights to inform future endeavors in LLM design and development."\n}\n\n
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\n Large language models (LLMs) serve as the foundation of numerous AI applications today. However, despite their remarkable proficiency in generating coherent text, questions linger regarding their ability in performing fine-grained linguistic annotation tasks, such as detecting nouns or verbs, or identifying more complex syntactic structures like clauses or T-units in input texts. These tasks require precise syntactic and semantic understanding of input text, and when LLMs underperform on specific linguistic structures, it raises concerns about their reliability for detailed linguistic analysis and whether their (even correct) outputs truly reflect an understanding of the inputs. In this paper, we empirically study recent LLMs performance across fine-grained linguistic annotation tasks. Through a series of experiments, we find that recent LLMs show limited efficacy in addressing linguistic queries and often struggle with linguistically complex inputs. We show that the most capable LLM (Llama3-70b) makes notable errors in detecting linguistic structures, such as misidentifying embedded clauses, failing to recognize verb phrases, and confusing complex nominals with clauses. Our study provides valuable insights to inform future endeavors in LLM design and development.\n
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\n \n\n \n \n \n \n \n \n Can LLMs help create grammar?: Automating grammar creation for endangered languages with in-context learning.\n \n \n \n \n\n\n \n Spencer, P. T.; and Kongborrirak, N.\n\n\n \n\n\n\n In Rambow, O.; Wanner, L.; Apidianaki, M.; Al-Khalifa, H.; Eugenio, B. D.; and Schockaert, S., editor(s), Proc. of COLING, 2025. \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 
@inproceedings{spencer-25,\n\ttitle = {Can {LLMs} help create grammar?: {A}utomating grammar creation for endangered languages with in-context learning},\n\turl = {https://aclanthology.org/2025.coling-main.681/},\n\tbooktitle = {Proc. of {COLING}},\n\tauthor = {Spencer, Piyapath T. and Kongborrirak, Nanthipat},\n\teditor = {Rambow, Owen and Wanner, Leo and Apidianaki, Marianna and {Al-Khalifa}, Hend and Eugenio, Barbara Di and Schockaert, Steven},\n\tyear = {2025}\n}
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\n  \n 2024\n \n \n (10)\n \n \n
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\n \n\n \n \n \n \n \n \n Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks.\n \n \n \n \n\n\n \n Herrera, S.; Corro, C.; and Kahane, S.\n\n\n \n\n\n\n March 2024.\n arXiv:2403.17534 [cs] version: 1\n\n\n\n
\n\n\n\n \n \n \"SparsePaper\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{herrera-24,\n\ttitle = {Sparse {L}ogistic {R}egression with {H}igh-order {F}eatures for {A}utomatic {G}rammar {R}ule {E}xtraction from {T}reebanks},\n\turl = {http://arxiv.org/abs/2403.17534},\n\tpublisher = {{arXiv}},\n\tauthor = {Herrera, Santiago and Corro, Caio and Kahane, Sylvain},\n\tmonth = mar,\n\tyear = {2024},\n\tnote = {{arXiv}:2403.17534 [cs]\nversion: 1}\n}
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\n \n\n \n \n \n \n \n \n CuRIAM: Corpus Re Interpretation and Metalanguage in U.S. Supreme Court Opinions.\n \n \n \n \n\n\n \n Kranzlein, M.; Schneider, N.; and Tobia, K.\n\n\n \n\n\n\n In Calzolari, N.; Kan, M.; Hoste, V.; Lenci, A.; Sakti, S.; and Xue, N., editor(s), Proc. of LREC-COLING, 2024. \n \n\n\n\n
\n\n\n\n \n \n \"CuRIAM: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{kranzlein-24,\n\ttitle = {{CuRIAM}: {C}orpus {R}e {I}nterpretation and {M}etalanguage in {U.{S}.} {S}upreme {C}ourt {O}pinions},\n\turl = {https://aclanthology.org/2024.lrec-main.379},\n\tbooktitle = {Proc. of {LREC-COLING}},\n\tauthor = {Kranzlein, Michael and Schneider, Nathan and Tobia, Kevin},\n\teditor = {Calzolari, Nicoletta and Kan, {Min-Yen} and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},\n\tyear = {2024}\n}
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\n \n\n \n \n \n \n \n \n WordNet under scrutiny: Dictionary examples in the era of large language models.\n \n \n \n \n\n\n \n Almeman, F. Y.; Schockaert, S.; and Espinosa Anke, L.\n\n\n \n\n\n\n In Calzolari, N.; Kan, M.; Hoste, V.; Lenci, A.; Sakti, S.; and Xue, N., editor(s), Proc. of LREC-COLING, 2024. \n \n\n\n\n
\n\n\n\n \n \n \"WordNetPaper\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{almeman-24,\n\ttitle = {{WordNet} under scrutiny: {D}ictionary examples in the era of large language models},\n\turl = {https://aclanthology.org/2024.lrec-main.1538},\n\tbooktitle = {Proc. of {LREC-COLING}},\n\tauthor = {Almeman, Fatemah Yousef and Schockaert, Steven and Espinosa Anke, Luis},\n\teditor = {Calzolari, Nicoletta and Kan, {Min-Yen} and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},\n\tyear = {2024}\n}
\n
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\n \n\n \n \n \n \n \n \n A benchmark for learning to translate a new language from one grammar book.\n \n \n \n \n\n\n \n Tanzer, G.; Suzgun, M.; Visser, E.; Jurafsky, D.; and Melas-Kyriazi, L.\n\n\n \n\n\n\n February 2024.\n arXiv:2309.16575 [cs]\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 
@misc{tanzer-24,\n\ttitle = {A benchmark for learning to translate a new language from one grammar book},\n\turl = {http://arxiv.org/abs/2309.16575},\n\tpublisher = {{arXiv}},\n\tauthor = {Tanzer, Garrett and Suzgun, Mirac and Visser, Eline and Jurafsky, Dan and {Melas-Kyriazi}, Luke},\n\tmonth = feb,\n\tyear = {2024},\n\tnote = {{arXiv}:2309.16575 [cs]}\n}
\n
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\n \n\n \n \n \n \n \n \n AMenDeD: Modelling concepts by aligning mentions, definitions and decontextualised embeddings.\n \n \n \n \n\n\n \n Gajbhiye, A.; Bouraoui, Z.; Espinosa Anke, L.; and Schockaert, S.\n\n\n \n\n\n\n In Calzolari, N.; Kan, M.; Hoste, V.; Lenci, A.; Sakti, S.; and Xue, N., editor(s), Proc. of LREC-COLING, 2024. \n \n\n\n\n
\n\n\n\n \n \n \"AMenDeD: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{gajbhiye-24,\n\ttitle = {{AMenDeD}: {M}odelling concepts by aligning mentions, definitions and decontextualised embeddings},\n\turl = {https://aclanthology.org/2024.lrec-main.72},\n\tbooktitle = {Proc. of {LREC-COLING}},\n\tauthor = {Gajbhiye, Amit and Bouraoui, Zied and Espinosa Anke, Luis and Schockaert, Steven},\n\teditor = {Calzolari, Nicoletta and Kan, {Min-Yen} and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},\n\tyear = {2024}\n}
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\n \n\n \n \n \n \n \n \n I am a strange dataset: metalinguistic tests for language models.\n \n \n \n \n\n\n \n Thrush, T.; Moore, J.; Monares, M.; Potts, C.; and Kiela, D.\n\n\n \n\n\n\n In Ku, L.; Martins, A.; and Srikumar, V., editor(s), Proc. of ACL, 2024. \n \n\n\n\n
\n\n\n\n \n \n \"IPaper\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{thrush-24,\n\ttitle = {I am a strange dataset: metalinguistic tests for language models},\n\turl = {https://aclanthology.org/2024.acl-long.482},\n\tbooktitle = {Proc. of {ACL}},\n\tauthor = {Thrush, Tristan and Moore, Jared and Monares, Miguel and Potts, Christopher and Kiela, Douwe},\n\teditor = {Ku, {Lun-Wei} and Martins, Andre and Srikumar, Vivek},\n\tyear = {2024}\n}
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\n \n\n \n \n \n \n \n \n Tailored definitions with easy reach: complexity-controllable definition generation.\n \n \n \n \n\n\n \n Yang, L.; Yuan, J.; Kong, C.; Yu, J.; Chong, R.; Liu, Z.; and Yang, E.\n\n\n \n\n\n\n IEEE Transactions on Big Data,1–12. 2024.\n Conference Name: IEEE Transactions on Big Data\n\n\n\n
\n\n\n\n \n \n \"TailoredPaper\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{yang-24,\n\ttitle = {Tailored definitions with easy reach: complexity-controllable definition generation},\n\turl = {https://ieeexplore.ieee.org/abstract/document/10816507},\n\tjournal = {{IEEE} Transactions on Big Data},\n\tauthor = {Yang, Liner and Yuan, Jiaxin and Kong, Cunliang and Yu, Jingsi and Chong, Ruining and Liu, Zhenghao and Yang, Erhong},\n\tyear = {2024},\n\tnote = {Conference Name: {IEEE} Transactions on Big Data},\n\tpages = {1--12}\n}
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\n \n\n \n \n \n \n \n \n ModeLing: a novel dataset for testing linguistic reasoning in language models.\n \n \n \n \n\n\n \n Chi, N.; Malchev, T.; Kong, R.; Chi, R.; Huang, L.; Chi, E.; McCoy, R.; and Radev, D.\n\n\n \n\n\n\n In Hahn, M.; Sorokin, A.; Kumar, R.; Shcherbakov, A.; Otmakhova, Y.; Yang, J.; Serikov, O.; Rani, P.; Ponti, E. M.; Muradoğlu, S.; Gao, R.; Cotterell, R.; and Vylomova, E., editor(s), Proc. of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP, 2024. \n \n\n\n\n
\n\n\n\n \n \n \"ModeLing: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{chi-24,\n\ttitle = {{ModeLing}: a novel dataset for testing linguistic reasoning in language models},\n\turl = {https://aclanthology.org/2024.sigtyp-1.14/},\n\tbooktitle = {Proc. of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual {NLP}},\n\tauthor = {Chi, Nathan and Malchev, Teodor and Kong, Riley and Chi, Ryan and Huang, Lucas and Chi, Ethan and {McCoy}, R. and Radev, Dragomir},\n\teditor = {Hahn, Michael and Sorokin, Alexey and Kumar, Ritesh and Shcherbakov, Andreas and Otmakhova, Yulia and Yang, Jinrui and Serikov, Oleg and Rani, Priya and Ponti, Edoardo M. and Muradoğlu, Saliha and Gao, Rena and Cotterell, Ryan and Vylomova, Ekaterina},\n\tyear = {2024}\n}
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\n \n\n \n \n \n \n \n \n Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?.\n \n \n \n \n\n\n \n Aycock, S.; Stap, D.; Wu, D.; Monz, C.; and Sima'an, K.\n\n\n \n\n\n\n September 2024.\n arXiv:2409.19151 [cs]\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 
@misc{aycock-24,\n\ttitle = {Can {LLMs} {R}eally {L}earn to {T}ranslate a {Low-Resource} {L}anguage from {O}ne {G}rammar {B}ook?},\n\turl = {http://arxiv.org/abs/2409.19151},\n\tpublisher = {{arXiv}},\n\tauthor = {Aycock, Seth and Stap, David and Wu, Di and Monz, Christof and Sima'an, Khalil},\n\tmonth = sep,\n\tyear = {2024},\n\tnote = {{arXiv}:2409.19151 [cs]}\n}\n
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\n \n\n \n \n \n \n \n \n To Ask LLMs about English Grammaticality, Prompt Them in a Different Language.\n \n \n \n \n\n\n \n Behzad, S.; Zeldes, A.; and Schneider, N.\n\n\n \n\n\n\n In Al-Onaizan, Y.; Bansal, M.; and Chen, Y., editor(s), Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15622–15634, Miami, Florida, USA, November 2024. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"ToPaper\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{behzad-etal-2024-ask,\n    title = "To Ask {LLM}s about {E}nglish Grammaticality, Prompt Them in a Different Language",\n    author = "Behzad, Shabnam  and\n      Zeldes, Amir  and\n      Schneider, Nathan",\n    editor = "Al-Onaizan, Yaser  and\n      Bansal, Mohit  and\n      Chen, Yun-Nung",\n    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",\n    month = nov,\n    year = "2024",\n    address = "Miami, Florida, USA",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/2024.findings-emnlp.916/",\n    doi = "10.18653/v1/2024.findings-emnlp.916",\n    pages = "15622--15634",\n    abstract = "In addition to asking questions about facts in the world, some internet users{---}in particular, second language learners{---}ask questions about language itself. Depending on their proficiency level and audience, they may pose these questions in an L1 (first language) or an L2 (second language). We investigate how multilingual LLMs perform at crosslingual metalinguistic question answering. Focusing on binary questions about sentence grammaticality constructed from error-annotated learner corpora, we prompt three LLMs (Aya, Llama, and GPT) in multiple languages, including English, German, Korean, Russian, and Ukrainian. Our study reveals that the language of the prompt can significantly affect model performance, and despite English being the dominant training language for all three models, prompting in a different language with questions about English often yields better results."\n}\n
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\n\n\n
\n In addition to asking questions about facts in the world, some internet users—in particular, second language learners—ask questions about language itself. Depending on their proficiency level and audience, they may pose these questions in an L1 (first language) or an L2 (second language). We investigate how multilingual LLMs perform at crosslingual metalinguistic question answering. Focusing on binary questions about sentence grammaticality constructed from error-annotated learner corpora, we prompt three LLMs (Aya, Llama, and GPT) in multiple languages, including English, German, Korean, Russian, and Ukrainian. Our study reveals that the language of the prompt can significantly affect model performance, and despite English being the dominant training language for all three models, prompting in a different language with questions about English often yields better results.\n
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\n  \n 2023\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Large Linguistic Models: Analyzing theoretical linguistic abilities of LLMs.\n \n \n \n \n\n\n \n Beguš, G.; Dąbkowski, M.; and Rhodes, R.\n\n\n \n\n\n\n May 2023.\n arXiv:2305.00948 [cs]\n\n\n\n
\n\n\n\n \n \n \"LargePaper\n  \n \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 
@misc{begus-23,\n\ttitle = {Large {L}inguistic {M}odels: {A}nalyzing theoretical linguistic abilities of {LLMs}},\n\turl = {http://arxiv.org/abs/2305.00948},\n\tpublisher = {{arXiv}},\n\tauthor = {Beguš, Gašper and Dąbkowski, Maksymilian and Rhodes, Ryan},\n\tmonth = may,\n\tyear = {2023},\n\tnote = {{arXiv}:2305.00948 [cs]}\n}
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\n \n\n \n \n \n \n \n \n Linguistics for legal interpretation.\n \n \n \n \n\n\n \n Carney, T. R.\n\n\n \n\n\n\n UJ Press, July 2023.\n Publication Title: UJ Press DOI: 10.36615/9781776438891\n\n\n\n
\n\n\n\n \n \n \"LinguisticsPaper\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{carney-23,\n\ttitle = {Linguistics for legal interpretation},\n\turl = {https://ujonlinepress.uj.ac.za/index.php/ujp/catalog/download/174/495/1656},\n\tpublisher = {{UJ} Press},\n\tauthor = {Carney, Terrence R.},\n\tmonth = jul,\n\tyear = {2023},\n\tnote = {Publication Title: {UJ} Press\n{DOI}: 10.36615/9781776438891}\n}
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\n \n\n \n \n \n \n \n \n ELQA: A corpus of metalinguistic questions and answers about English.\n \n \n \n \n\n\n \n Behzad, S.; Sakaguchi, K.; Schneider, N.; and Zeldes, A.\n\n\n \n\n\n\n In Proc. of ACL, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"ELQA: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
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@inproceedings{behzad-23,\n\ttitle = {{ELQA}: {A} corpus of metalinguistic questions and answers about {E}nglish},\n\turl = {https://aclanthology.org/2023.acl-long.113},\n\tbooktitle = {Proc. of {ACL}},\n\tauthor = {Behzad, Shabnam and Sakaguchi, Keisuke and Schneider, Nathan and Zeldes, Amir},\n\tyear = {2023}\n}
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\n \n\n \n \n \n \n \n \n Prompting is not a substitute for probability measurements in large language models.\n \n \n \n \n\n\n \n Hu, J.; and Levy, R.\n\n\n \n\n\n\n In Bouamor, H.; Pino, J.; and Bali, K., editor(s), Proc. of EMNLP, 2023. \n \n\n\n\n
\n\n\n\n \n \n \"PromptingPaper\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{hu-23,\n\ttitle = {Prompting is not a substitute for probability measurements in large language models},\n\turl = {https://aclanthology.org/2023.emnlp-main.306},\n\tbooktitle = {Proc. of {EMNLP}},\n\tauthor = {Hu, Jennifer and Levy, Roger},\n\teditor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},\n\tyear = {2023}\n}
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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Models in a spelling bee: Language models implicitly learn the character composition of tokens.\n \n \n \n \n\n\n \n Itzhak, I.; and Levy, O.\n\n\n \n\n\n\n In Proc. of NAACL-HLT, 2022. \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
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@inproceedings{itzhak-22,\n\ttitle = {Models in a spelling bee: {L}anguage models implicitly learn the character composition of tokens},\n\turl = {https://aclanthology.org/2022.naacl-main.373},\n\tbooktitle = {Proc. of {NAACL-HLT}},\n\tauthor = {Itzhak, Itay and Levy, Omer},\n\tyear = {2022}\n}
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\n  \n 2021\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Decrypting cryptic crosswords: semantically complex wordplay puzzles as a target for NLP.\n \n \n \n \n\n\n \n Rozner, J.; Potts, C.; and Mahowald, K.\n\n\n \n\n\n\n . April 2021.\n \n\n\n\n
\n\n\n\n \n \n \"DecryptingPaper\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{rozner-21,\n\ttitle = {Decrypting cryptic crosswords: semantically complex wordplay puzzles as a target for {NLP}},\n\turl = {https://arxiv.org/abs/2104.08620v3},\n\tauthor = {Rozner, Joshua and Potts, Christopher and Mahowald, Kyle},\n\tmonth = apr,\n\tyear = {2021}\n}
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\n \n\n \n \n \n \n \n \n Exemplification modeling: Can you give me an example, please?.\n \n \n \n \n\n\n \n Barba, E.; Procopio, L.; Lacerra, C.; Pasini, T.; and Navigli, R.\n\n\n \n\n\n\n In volume 4, 2021. \n ISSN: 1045-0823\n\n\n\n
\n\n\n\n \n \n \"ExemplificationPaper\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{barba-21,\n\ttitle = {Exemplification modeling: {C}an you give me an example, please?},\n\tvolume = {4},\n\turl = {https://www.ijcai.org/proceedings/2021/520},\n\tauthor = {Barba, Edoardo and Procopio, Luigi and Lacerra, Caterina and Pasini, Tommaso and Navigli, Roberto},\n\tyear = {2021},\n\tnote = {{ISSN}: 1045-0823}\n}
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\n \n\n \n \n \n \n \n \n A deep learning system for automatic extraction of typological linguistic information from descriptive grammars.\n \n \n \n \n\n\n \n Virk, S. M.; Foster, D.; Sheikh Muhammad, A.; and Saleem, R.\n\n\n \n\n\n\n In Proc. of RANLP, 2021. \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
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@inproceedings{virk-21,\n\ttitle = {A deep learning system for automatic extraction of typological linguistic information from descriptive grammars},\n\turl = {https://aclanthology.org/2021.ranlp-1.166},\n\tbooktitle = {Proc. of {RANLP}},\n\tauthor = {Virk, Shafqat Mumtaz and Foster, Daniel and Sheikh Muhammad, Azam and Saleem, Raheela},\n\tyear = {2021}\n}
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\n \n\n \n \n \n \n \n \n A corpus-based examination of reflexive metadiscourse in majority and dissent opinions of the U.S. Supreme Court.\n \n \n \n \n\n\n \n McKeown, J.\n\n\n \n\n\n\n Journal of Pragmatics, 186: 224–235. December 2021.\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{mckeown-21,\n\ttitle = {A corpus-based examination of reflexive metadiscourse in majority and dissent opinions of the {U.{S}.} {S}upreme {C}ourt},\n\tvolume = {186},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0378216621003593},\n\tjournal = {Journal of Pragmatics},\n\tauthor = {{McKeown}, Jamie},\n\tmonth = dec,\n\tyear = {2021},\n\tpages = {224--235}\n}
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\n  \n 2020\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Bridging Anaphora Resolution as Question Answering.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n In null, editor(s), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 2020. Association for Computational Linguistics\n \n\n\n\n
\n\n\n\n \n \n \"BridgingPaper\n  \n \n \n \"Bridging arxiv\n  \n \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
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\n  \n\n  \n  \n  \n  \n  \n  \n    PuzzLing Machines: a challenge on learning from small data.\n  \n  \n  \n  \n\n\n  \n  Şahin, G. G.; Kementchedjhieva, Y.; Rust, P.;  and Gurevych, I.\n\n\n  \n\n\n\n  In Proc. of ACL,  2020. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"PuzzLingPaper\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{sahin-20,\n\ttitle = {{PuzzLing} {M}achines: a challenge on learning from small data},\n\turl = {https://www.aclweb.org/anthology/2020.acl-main.115},\n\tbooktitle = {Proc. of {ACL}},\n\tauthor = {Şahin, Gözde Gül and Kementchedjhieva, Yova and Rust, Phillip and Gurevych, Iryna},\n\tyear = {2020}\n}
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\n  \n\n  \n  \n  \n  \n  \n  \n    The DReaM Corpus: A multilingual annotated corpus of grammars for the world's languages.\n  \n  \n  \n  \n\n\n  \n  Virk, S. M.; Hammarström, H.; Forsberg, M.;  and Wichmann, S.\n\n\n  \n\n\n\n  In Proc. of LREC,  2020. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"ThePaper\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{virk-20,\n\ttitle = {The {DReaM} {C}orpus: {A} multilingual annotated corpus of grammars for the world's languages},\n\turl = {https://aclanthology.org/2020.lrec-1.110},\n\tbooktitle = {Proc. of {LREC}},\n\tauthor = {Virk, Shafqat Mumtaz and Hammarström, Harald and Forsberg, Markus and Wichmann, Søren},\n\tyear = {2020}\n}
\n
\n\n\n\n
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\n  \n\n  \n  \n  \n  \n  \n  \n    Automatic extraction of rules governing morphological agreement.\n  \n  \n  \n  \n\n\n  \n  Chaudhary, A.; Anastasopoulos, A.; Pratapa, A.; Mortensen, D. R.; Sheikh, Z.; Tsvetkov, Y.;  and Neubig, G.\n\n\n  \n\n\n\n  In Webber, B.; Cohn, T.; He, Y.;  and Liu, Y., editor(s), Proc. of EMNLP,  2020. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"AutomaticPaper\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{chaudhary-20,\n\ttitle = {Automatic extraction of rules governing morphological agreement},\n\turl = {https://aclanthology.org/2020.emnlp-main.422},\n\tbooktitle = {Proc. of {EMNLP}},\n\tauthor = {Chaudhary, Aditi and Anastasopoulos, Antonios and Pratapa, Adithya and Mortensen, David R. and Sheikh, Zaid and Tsvetkov, Yulia and Neubig, Graham},\n\teditor = {Webber, Bonnie and Cohn, Trevor and He, Yulan and Liu, Yang},\n\tyear = {2020}\n}
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\n    
\n       \n      2018\n      \n        \n        (1)\n        \n      \n    
\n    
\n      \n  \n  
\n  \n\n  \n  \n  \n  \n  \n  LingFN: Towards a framenet for the linguistics domain.\n  \n  \n  \n\n\n  \n  Malm, P.; Mumtaz Virk, S.; Borin, L.;  and Saxena, A.\n\n\n  \n\n\n\n  In Proc. of the LREC 2018 Workshop International FrameNet Workshop 2018: Multilingual Framenets and Constructicons,  2018. \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{malm-18,\n\ttitle = {{LingFN}: {T}owards a framenet for the linguistics domain},\n\tbooktitle = {Proc. of the {LREC} 2018 Workshop International {FrameNet} Workshop 2018: Multilingual Framenets and Constructicons},\n\tauthor = {Malm, Per and Mumtaz Virk, Shafqat and Borin, Lars and Saxena, Anju},\n\tyear = {2018}\n}
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\n    
\n       \n      2013\n      \n        \n        (1)\n        \n      \n    
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\n  \n\n  \n  \n  \n  \n  \n  \n    Toward automatic processing of English metalanguage.\n  \n  \n  \n  \n\n\n  \n  Wilson, S.\n\n\n  \n\n\n\n  In Proc. of IJCNLP,  2013. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"TowardPaper\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{wilson-13,\n\ttitle = {Toward automatic processing of {E}nglish metalanguage},\n\turl = {https://www.aclweb.org/anthology/I13-1091},\n\tbooktitle = {Proc. of {IJCNLP}},\n\tauthor = {Wilson, Shomir},\n\tyear = {2013}\n}
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\n    
\n       \n      2012\n      \n        \n        (1)\n        \n      \n    
\n    
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\n  \n\n  \n  \n  \n  \n  \n  \n    The creation of a corpus of English metalanguage.\n  \n  \n  \n  \n\n\n  \n  Wilson, S.\n\n\n  \n\n\n\n  In Proc. of ACL,  2012. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"ThePaper\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{wilson-12,\n\ttitle = {The creation of a corpus of {E}nglish metalanguage},\n\turl = {https://aclanthology.org/P12-1067},\n\tbooktitle = {Proc. of {ACL}},\n\tauthor = {Wilson, Shomir},\n\tyear = {2012}\n}
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\n\n\n\n
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\n       \n      2010\n      \n        \n        (1)\n        \n      \n    
\n    
\n      \n  \n  
\n  \n\n  \n  \n  \n  \n  \n  \n    Distinguishing use and mention in natural language.\n  \n  \n  \n  \n\n\n  \n  Wilson, S.\n\n\n  \n\n\n\n  In Proc. of the NAACL HLT 2010 Student Research Workshop,  2010. \n  \n\n\n\n
\n\n\n\n  \n  \n    \"DistinguishingPaper\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{wilson-10,\n\ttitle = {Distinguishing use and mention in natural language},\n\turl = {https://aclanthology.org/N10-3006},\n\tbooktitle = {Proc. of the {NAACL} {HLT} 2010 Student Research Workshop},\n\tauthor = {Wilson, Shomir},\n\tyear = {2010}\n}
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\n       \n      2005\n      \n        \n        (1)\n        \n      \n    
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\n  \n\n  \n  \n  \n  \n  \n  \n    Making the most of metalanguage.\n  \n  \n  \n  \n\n\n  \n  Berry, R.\n\n\n  \n\n\n\n  Language Awareness, 14(1): 3–20. February 2005.\n  \n\n\n\n
\n\n\n\n  \n  \n    \"MakingPaper\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{berry-05,\n\ttitle = {Making the most of metalanguage},\n\tvolume = {14},\n\turl = {http://www.tandfonline.com/doi/abs/10.1080/09658410508668817},\n\tnumber = {1},\n\tjournal = {Language Awareness},\n\tauthor = {Berry, Roger},\n\tmonth = feb,\n\tyear = {2005},\n\tpages = {3--20}\n}
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