Verb Metaphor Detection via Contextual Relation Learning

Wei Song, Shuhui Zhou, Ruiji Fu, Ting Liu, Lizhen Liu


Abstract
Correct natural language understanding requires computers to distinguish the literal and metaphorical senses of a word. Recent neu- ral models achieve progress on verb metaphor detection by viewing it as sequence labeling. In this paper, we argue that it is appropriate to view this task as relation classification between a verb and its various contexts. We propose the Metaphor-relation BERT (Mr-BERT) model, which explicitly models the relation between a verb and its grammatical, sentential and semantic contexts. We evaluate our method on the VUA, MOH-X and TroFi datasets. Our method gets competitive results compared with state-of-the-art approaches.
Anthology ID:
2021.acl-long.327
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4240–4251
Language:
URL:
https://aclanthology.org/2021.acl-long.327
DOI:
10.18653/v1/2021.acl-long.327
Bibkey:
Cite (ACL):
Wei Song, Shuhui Zhou, Ruiji Fu, Ting Liu, and Lizhen Liu. 2021. Verb Metaphor Detection via Contextual Relation Learning. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4240–4251, Online. Association for Computational Linguistics.
Cite (Informal):
Verb Metaphor Detection via Contextual Relation Learning (Song et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.327.pdf
Video:
 https://aclanthology.org/2021.acl-long.327.mp4