MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories

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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.

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Choi, M., Lee, S., Choi, E., Park, H., Lee, J., Lee, D., & Lee, J. (2021). MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories. In NAACL-HLT 2021 - 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (pp. 1763–1773). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.naacl-main.141

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