TRR 318 - Subproject C4 - Metaphors as an explanation tool
Overview
Project C04 investigates metaphors as a specific means to make difficult phenomena interpretable. The innovative aspect is to regard both highlighting and hiding processes when metaphors are utilized in explanations. The project aims to understand how metaphors may either facilitate or impede understanding, and how this understanding can be applied in AI systems to construct metaphors. The project contributes to our understanding of explanation spaces and to the principles by which explanations are generated or tailored to the needs of an addressee by making choices in the explanation space, that is, by systematically highlighting and hiding aspects of the explanandum.
More Information
Publications
Analyzing the Use of Metaphors in News Editorials for Political Framing
M. Sengupta, R. El Baff, M. Alshomary, H. Wachsmuth, in: K. Duh, H. Gomez, S. Bethard (Eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Association for Computational Linguistics, Mexico City, Mexico, 2024, pp. 3621–3631.
Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms
M. Sengupta, M. Alshomary, I. Scharlau, H. Wachsmuth, in: Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, 2023.
Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning
M. Sengupta, M. Alshomary, H. Wachsmuth, in: Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), Association for Computational Linguistics, 2023.
Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms
M. Sengupta, M. Alshomary, I. Scharlau, H. Wachsmuth, in: H. Bouamor, J. Pino, K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore, 2023, pp. 4636–4659.
Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning
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M. Sengupta, M. Alshomary, H. Wachsmuth, in: Proceedings of the 2022 Workshop on Figurative Language Processing, 2022.