Abstract
We propose a new model for metaphor detection in which an expectation component estimates representations of expected word meanings in a given context, whereas a realization component computes representations of target word meanings in context. We also introduce a systematic evaluation methodology that estimates generalization performance in three settings: within distribution, a new strong out of distribution setting, and a novel out-of-pretraining setting. Across all settings, the expectation-realization model obtains results that are competitive with or better than previous metaphor detection models.- Anthology ID:
- 2024.figlang-1.11
- Volume:
- Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico (Hybrid)
- Editors:
- Debanjan Ghosh, Smaranda Muresan, Anna Feldman, Tuhin Chakrabarty, Emmy Liu
- Venues:
- Fig-Lang | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 79–84
- Language:
- URL:
- https://aclanthology.org/2024.figlang-1.11
- DOI:
- Cite (ACL):
- Oseremen Uduehi and Razvan Bunescu. 2024. An Expectation-Realization Model for Metaphor Detection: Within Distribution, Out of Distribution, and Out of Pretraining. In Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024), pages 79–84, Mexico City, Mexico (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- An Expectation-Realization Model for Metaphor Detection: Within Distribution, Out of Distribution, and Out of Pretraining (Uduehi & Bunescu, Fig-Lang-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.figlang-1.11.pdf