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Creators/Authors contains: "Liu, Emmy"

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  1. ; ; (, Conference on Language Modeling)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; (, Association for Computational Linguistics)
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  3. ; ; ; ; (, Association for Computational Linguistics)
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  4. ; ; ; ; (, Association for Computational Linguistics)
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  5. ; ; ; ; (, International Conference on Learning Representations)
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  6. ; ; ; (, Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023))
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  7. ; ; ; (, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)
    Figurative and metaphorical language are commonplace in discourse, and figurative expressions play an important role in communication and cognition. However, figurative language has been a relatively under-studied area in NLP, and it remains an open question to what extent modern language models can interpret nonliteral phrases. To address this question, we introduce Fig-QA, a Winograd-style nonliteral language understanding task consisting of correctly interpreting paired figurative phrases with divergent meanings. We evaluate the performance of several state-of-the-art language models on this task, and find that although language models achieve performance significantly over chance, they still fall short of human performance, particularly in zero- or few-shot settings. This suggests that further work is needed to improve the nonliteral reasoning capabilities of language models. 
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