SBU Figures It Out: Models Explain Figurative Language

Yash Kumar Lal, Mohaddeseh Bastan


Abstract
Figurative language is ubiquitous in human communication. However, current NLP models are unable to demonstrate a significant understanding of instances of this phenomena. The EMNLP 2022 shared task on figurative language understanding posed the problem of predicting and explaining the relation between a premise and a hypothesis containing an instance of the use of figurative language. We experiment with different variations of using T5-large for this task and build a model that significantly outperforms the task baseline. Treating it as a new task for T5 and simply finetuning on the data achieves the best score on the defined evaluation. Furthermore, we find that hypothesis-only models are able to achieve most of the performance.
Anthology ID:
2022.flp-1.20
Volume:
Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Debanjan Ghosh, Beata Beigman Klebanov, Smaranda Muresan, Anna Feldman, Soujanya Poria, Tuhin Chakrabarty
Venue:
Fig-Lang
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
143–149
Language:
URL:
https://aclanthology.org/2022.flp-1.20
DOI:
10.18653/v1/2022.flp-1.20
Bibkey:
Cite (ACL):
Yash Kumar Lal and Mohaddeseh Bastan. 2022. SBU Figures It Out: Models Explain Figurative Language. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), pages 143–149, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
SBU Figures It Out: Models Explain Figurative Language (Lal & Bastan, Fig-Lang 2022)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.flp-1.20.pdf