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

Rui Mao, Chenghua Lin, Frank Guerin


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.
Anthology ID:
P19-1378
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3888–3898
Language:
URL:
https://aclanthology.org/P19-1378
DOI:
10.18653/v1/P19-1378
Bibkey:
Cite (ACL):
Rui Mao, Chenghua Lin, and Frank Guerin. 2019. End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3888–3898, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories (Mao et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1378.pdf
Code
 RuiMao1988/Sequential-Metaphor-Identification