@inproceedings{uduehi-bunescu-2024-expectation,
title = "An Expectation-Realization Model for Metaphor Detection",
author = "Uduehi, Oseremen and
Bunescu, Razvan",
editor = "Ghosh, Debanjan and
Muresan, Smaranda and
Feldman, Anna and
Chakrabarty, Tuhin and
Liu, Emmy",
booktitle = "Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.figlang-1.11",
doi = "10.18653/v1/2024.figlang-1.11",
pages = "79--84",
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.",
}
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%0 Conference Proceedings
%T An Expectation-Realization Model for Metaphor Detection
%A Uduehi, Oseremen
%A Bunescu, Razvan
%Y Ghosh, Debanjan
%Y Muresan, Smaranda
%Y Feldman, Anna
%Y Chakrabarty, Tuhin
%Y Liu, Emmy
%S Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico (Hybrid)
%F uduehi-bunescu-2024-expectation
%X 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.
%R 10.18653/v1/2024.figlang-1.11
%U https://aclanthology.org/2024.figlang-1.11
%U https://doi.org/10.18653/v1/2024.figlang-1.11
%P 79-84
Markdown (Informal)
[An Expectation-Realization Model for Metaphor Detection](https://aclanthology.org/2024.figlang-1.11) (Uduehi & Bunescu, Fig-Lang-WS 2024)
ACL