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.
@article{arxiv.2106.01195,
title = {Figurative Language in Recognizing Textual Entailment},
author = {Tuhin Chakrabarty and Debanjan Ghosh and Adam Poliak and Smaranda Muresan},
journal= {arXiv preprint arXiv:2106.01195},
year = {2021}
}