@inproceedings{choi-etal-2021-melbert,
title = "{M}el{BERT}: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories",
author = "Choi, Minjin and
Lee, Sunkyung and
Choi, Eunseong and
Park, Heesoo and
Lee, Junhyuk and
Lee, Dongwon and
Lee, Jongwuk",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.141",
doi = "10.18653/v1/2021.naacl-main.141",
pages = "1763--1773",
abstract = "Automated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely \textit{metaphor-aware late interaction over BERT (MelBERT)}. Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to detect whether the target word is metaphorical. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.",
}
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<abstract>Automated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT). Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to detect whether the target word is metaphorical. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.</abstract>
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%0 Conference Proceedings
%T MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories
%A Choi, Minjin
%A Lee, Sunkyung
%A Choi, Eunseong
%A Park, Heesoo
%A Lee, Junhyuk
%A Lee, Dongwon
%A Lee, Jongwuk
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F choi-etal-2021-melbert
%X Automated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT). Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to detect whether the target word is metaphorical. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.
%R 10.18653/v1/2021.naacl-main.141
%U https://aclanthology.org/2021.naacl-main.141
%U https://doi.org/10.18653/v1/2021.naacl-main.141
%P 1763-1773
Markdown (Informal)
[MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories](https://aclanthology.org/2021.naacl-main.141) (Choi et al., NAACL 2021)
ACL