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Figurative Language in Recognizing Textual Entailment

2021-06-02 · Findings (ACL) 2021 8 · Tuhin Chakrabarty, Debanjan Ghosh, Adam Poliak, Smaranda Muresan

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.

📄 PDF Abstract BibTeX arXiv:2106.01195

Code (1)

tuhinjubcse/Figurative-NLI 공식 구현 pytorch

Tasks

Natural Language InferenceRTEWorld Knowledge

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