Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining

Yuan Tian, Ruike Zhang, Nan Xu, Wenji Mao


Abstract
Metaphor detection aims to identify whether a linguistic expression in text is metaphorical or literal. Most existing research tackles this problem either using word-pair or token-level information as input, and thus treats word-pair and token-level metaphor detection as distinct subtasks. Benefited from the simplified structure of word pairs, recent methods for word-pair metaphor detection can provide intermediate explainable clues for the detection results, which remains a challenging issue for token-level metaphor detection. To mitigate this issue in token-level metaphor detection and take advantage of word pairs, in this paper, we make the first attempt to bridge word-pair and token-level metaphor detection via modeling word pairs within a sentence as explainable intermediate information. As the central role of verb in metaphorical expressions, we focus on token-level verb metaphor detection and propose a novel explainable Word Pair based Domain Mining (WPDM) method. Our work is inspired by conceptual metaphor theory (CMT). We first devise an approach for conceptual domain mining utilizing semantic role mapping and resources at cognitive, commonsense and lexical levels. We then leverage the inconsistency between source and target domains for core word pair modeling to facilitate the explainability. Experiments on four datasets verify the effectiveness of our method and demonstrate its capability to provide the core word pair and corresponding conceptual domains as explainable clues for metaphor detection.
Anthology ID:
2024.acl-long.719
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13311–13325
Language:
URL:
https://aclanthology.org/2024.acl-long.719
DOI:
10.18653/v1/2024.acl-long.719
Bibkey:
Cite (ACL):
Yuan Tian, Ruike Zhang, Nan Xu, and Wenji Mao. 2024. Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13311–13325, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining (Tian et al., ACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.acl-long.719.pdf