MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories

Minjin Choi, Sunkyung Lee, Eunseong Choi, Heesoo Park, Junhyuk Lee, Dongwon Lee, Jongwuk Lee


Abstract
Automated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT). Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to detect whether the target word is metaphorical. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.
Anthology ID:
2021.naacl-main.141
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1763–1773
Language:
URL:
https://aclanthology.org/2021.naacl-main.141
DOI:
10.18653/v1/2021.naacl-main.141
Bibkey:
Cite (ACL):
Minjin Choi, Sunkyung Lee, Eunseong Choi, Heesoo Park, Junhyuk Lee, Dongwon Lee, and Jongwuk Lee. 2021. MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1763–1773, Online. Association for Computational Linguistics.
Cite (Informal):
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories (Choi et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.141.pdf
Code
 jin530/MelBERT