@inproceedings{wang-etal-2023-metaphor,
title = "Metaphor Detection with Effective Context Denoising",
author = "Wang, Shun and
Li, Yucheng and
Lin, Chenghua and
Barrault, Loic and
Guerin, Frank",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.102",
doi = "10.18653/v1/2023.eacl-main.102",
pages = "1404--1409",
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 \url{https://github.com/MajiBear000/RoPPT}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Metaphor Detection with Effective Context Denoising
%A Wang, Shun
%A Li, Yucheng
%A Lin, Chenghua
%A Barrault, Loic
%A Guerin, Frank
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F wang-etal-2023-metaphor
%X 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.
%R 10.18653/v1/2023.eacl-main.102
%U https://aclanthology.org/2023.eacl-main.102
%U https://doi.org/10.18653/v1/2023.eacl-main.102
%P 1404-1409
Markdown (Informal)
[Metaphor Detection with Effective Context Denoising](https://aclanthology.org/2023.eacl-main.102) (Wang et al., EACL 2023)
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.