End-to-end sequential metaphor identification inspired by linguistic theories

Mao, R., Lin, C. orcid.org/0000-0003-3454-2468 and Guerin, F. (2019) End-to-end sequential metaphor identification inspired by linguistic theories. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 57th Annual Meeting of the Association for Computational Linguistics, 28 Jul - 02 Aug 2019, Florence, Italy. Association for Computational Linguistics , pp. 3888-3898. ISBN 9781950737482

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.

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Authors/Creators:	Mao, R. Lin, C. https://orcid.org/0000-0003-3454-2468 Guerin, F.
Copyright, Publisher and Additional Information:	© 2019 Association for Computational Linguistics.
Dates:	Published (online): July 2019 Published: July 2019
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User:	Symplectic Sheffield
Date Deposited:	27 Jan 2020 10:53
Last Modified:	27 Jan 2020 10:53
Status:	Published
Publisher:	Association for Computational Linguistics
Refereed:	Yes
Identification Number:	https://doi.org/10.18653/v1/P19-1378
Related URLs:	Publisher

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End-to-end sequential metaphor identification inspired by linguistic theories

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