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Figurative Language in Recognizing Textual Entailment

  • Educational Testing Service
  • Columbia University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

35 Scopus citations

Abstract

We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language. We leverage five existing datasets annotated for a variety of figurative language - simile, metaphor, and irony - and frame them into over 12,500 RTE examples.We evaluate how well state-of-the-art models trained on popular RTE datasets capture different aspects of figurative language. Our results and analyses indicate that these models might not sufficiently capture figurative language, struggling to perform pragmatic inference and reasoning about world knowledge. Ultimately, our datasets provide a challenging testbed for evaluating RTE models.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationACL-IJCNLP 2021
EditorsChengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
PublisherAssociation for Computational Linguistics (ACL)
Pages3354-3361
Number of pages8
ISBN (Electronic)9781954085541
DOIs
StatePublished - 2021
EventFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021 - Virtual, Online
Duration: Aug 1 2021Aug 6 2021

Publication series

NameFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Conference

ConferenceFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021
CityVirtual, Online
Period08/1/2108/6/21

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