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Figurative Language in Recognizing Textual Entailment

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arxiv 2106.01195 v2 pith:DDT7IBFO submitted 2021-06-02 cs.CL cs.AI

Tuhin Chakrabarty , Debanjan Ghosh , Adam Poliak , Smaranda Muresan This is my paper · ORCID
classification cs.CLcs.AI
keywords figurativelanguagedatasetsmodelscaptureentailmentrecognizingtextual
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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.

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  1. Large Vision-Language Models for Knowledge-Grounded Data Annotation of Memes

    cs.LG 2025-01 conditional novelty 5.0 of 10

    CM50, a 33k-meme dataset with GPT-4o-generated annotations, and mtrCLIP, a fine-tuned CLIP model, together improve meme-text retrieval on MemeCap over the original CLIP.

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