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    <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.</abstract>
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%0 Conference Proceedings
%T Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning
%A Sengupta, Meghdut
%A Alshomary, Milad
%A Wachsmuth, Henning
%Y Ghosh, Debanjan
%Y Beigman Klebanov, Beata
%Y Muresan, Smaranda
%Y Feldman, Anna
%Y Poria, Soujanya
%Y Chakrabarty, Tuhin
%S Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F sengupta-etal-2022-back
%X 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.
%R 10.18653/v1/2022.flp-1.19
%U https://aclanthology.org/2022.flp-1.19/
%U https://doi.org/10.18653/v1/2022.flp-1.19
%P 137-142

Markdown (Informal)

[Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning](https://aclanthology.org/2022.flp-1.19/) (Sengupta et al., Fig-Lang 2022)

• Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning (Sengupta et al., Fig-Lang 2022)

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.