@inproceedings{song-etal-2021-verb,
title = "Verb Metaphor Detection via Contextual Relation Learning",
author = "Song, Wei and
Zhou, Shuhui and
Fu, Ruiji and
Liu, Ting and
Liu, Lizhen",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.327",
doi = "10.18653/v1/2021.acl-long.327",
pages = "4240--4251",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Verb Metaphor Detection via Contextual Relation Learning
%A Song, Wei
%A Zhou, Shuhui
%A Fu, Ruiji
%A Liu, Ting
%A Liu, Lizhen
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S 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)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F song-etal-2021-verb
%X 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.
%R 10.18653/v1/2021.acl-long.327
%U https://aclanthology.org/2021.acl-long.327
%U https://doi.org/10.18653/v1/2021.acl-long.327
%P 4240-4251
Markdown (Informal)
[Verb Metaphor Detection via Contextual Relation Learning](https://aclanthology.org/2021.acl-long.327) (Song et al., ACL-IJCNLP 2021)
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.