Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning

Meghdut Sengupta, Milad Alshomary, Henning Wachsmuth


Abstract
Metaphors frame a given target domain using concepts from another, usually more concrete, source domain. Previous research in NLP has focused on the identification of metaphors and the interpretation of their meaning. In contrast, this paper studies to what extent the source domain can be predicted computationally from a metaphorical text. Given a dataset with metaphorical texts from a finite set of source domains, we propose a contrastive learning approach that ranks source domains by their likelihood of being referred to in a metaphorical text. In experiments, it achieves reasonable performance even for rare source domains, clearly outperforming a classification baseline.
Anthology ID:
2022.flp-1.19
Volume:
Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Debanjan Ghosh, Beata Beigman Klebanov, Smaranda Muresan, Anna Feldman, Soujanya Poria, Tuhin Chakrabarty
Venue:
Fig-Lang
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
137–142
Language:
URL:
https://aclanthology.org/2022.flp-1.19
DOI:
10.18653/v1/2022.flp-1.19
Bibkey:
Cite (ACL):
Meghdut Sengupta, Milad Alshomary, and Henning Wachsmuth. 2022. Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), pages 137–142, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning (Sengupta et al., Fig-Lang 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.flp-1.19.pdf
Video:
 https://aclanthology.org/2022.flp-1.19.mp4