End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories

Rui Mao, Chenghua Lin, Frank Guerin

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Abstract

End-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor identification. We experiment with two DNN models which are inspired by two human metaphor identification procedures. By testing on three public datasets, we find that our models achieve state-of-the-art performance in end-to-end metaphor identification.
Original languageEnglish
Pages3888-3898
Number of pages11
DOIs
Publication statusPublished - Jul 2019
Event57th Annual Meeting of the Association for Computational Linguistics (ACL) - Fortezza da Basso, Florence, Italy
Duration: 28 Jul 20192 Aug 2019

Conference

Conference57th Annual Meeting of the Association for Computational Linguistics (ACL)
CountryItaly
CityFlorence
Period28/07/192/08/19

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Mao, R., Lin, C., & Guerin, F. (2019). End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories. 3888-3898. Paper presented at 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy. https://doi.org/10.18653/v1/P19-1378