An Expectation-Realization Model for Metaphor Detection

Oseremen Uduehi, Razvan Bunescu


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:
10.18653/v1/2024.figlang-1.11
Bibkey:
Cite (ACL):
Oseremen Uduehi and Razvan Bunescu. 2024. An Expectation-Realization Model for Metaphor Detection. 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 (Uduehi & Bunescu, Fig-Lang-WS 2024)
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PDF:
https://aclanthology.org/2024.figlang-1.11.pdf