@inproceedings{lal-bastan-2022-sbu,
title = "{SBU} Figures It Out: Models Explain Figurative Language",
author = "Lal, Yash Kumar and
Bastan, Mohaddeseh",
editor = "Ghosh, Debanjan and
Beigman Klebanov, Beata and
Muresan, Smaranda and
Feldman, Anna and
Poria, Soujanya and
Chakrabarty, Tuhin",
booktitle = "Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.flp-1.20",
doi = "10.18653/v1/2022.flp-1.20",
pages = "143--149",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SBU Figures It Out: Models Explain Figurative Language
%A Lal, Yash Kumar
%A Bastan, Mohaddeseh
%Y Ghosh, Debanjan
%Y Beigman Klebanov, Beata
%Y Muresan, Smaranda
%Y Feldman, Anna
%Y Poria, Soujanya
%Y Chakrabarty, Tuhin
%S Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F lal-bastan-2022-sbu
%X 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.
%R 10.18653/v1/2022.flp-1.20
%U https://aclanthology.org/2022.flp-1.20
%U https://doi.org/10.18653/v1/2022.flp-1.20
%P 143-149
Markdown (Informal)
[SBU Figures It Out: Models Explain Figurative Language](https://aclanthology.org/2022.flp-1.20) (Lal & Bastan, Fig-Lang 2022)
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.