Metaphor Detection with Effective Context Denoising

Shun Wang, Yucheng Li, Chenghua Lin, Loic Barrault, Frank Guerin


Abstract
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection. Compared to existing models, RoPPT focuses on semantically relevant information and achieves the state-of-the-art on several main metaphor datasets. We also compare our approach against several popular denoising and pruning methods, demonstrating the effectiveness of our approach in context denoising. Our code and dataset can be found at https://github.com/MajiBear000/RoPPT.
Anthology ID:
2023.eacl-main.102
Volume:
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
Month:
May
Year:
2023
Address:
Dubrovnik, Croatia
Editors:
Andreas Vlachos, Isabelle Augenstein
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1404–1409
Language:
URL:
https://aclanthology.org/2023.eacl-main.102
DOI:
10.18653/v1/2023.eacl-main.102
Bibkey:
Cite (ACL):
Shun Wang, Yucheng Li, Chenghua Lin, Loic Barrault, and Frank Guerin. 2023. Metaphor Detection with Effective Context Denoising. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1404–1409, Dubrovnik, Croatia. Association for Computational Linguistics.
Cite (Informal):
Metaphor Detection with Effective Context Denoising (Wang et al., EACL 2023)
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PDF:
https://aclanthology.org/2023.eacl-main.102.pdf
Video:
 https://aclanthology.org/2023.eacl-main.102.mp4