Neural Multitask Learning for Simile Recognition

Lizhen Liu, Xiao Hu, Wei Song, Ruiji Fu, Ting Liu, Guoping Hu


Abstract
Simile is a special type of metaphor, where comparators such as like and as are used to compare two objects. Simile recognition is to recognize simile sentences and extract simile components, i.e., the tenor and the vehicle. This paper presents a study of simile recognition in Chinese. We construct an annotated corpus for this research, which consists of 11.3k sentences that contain a comparator. We propose a neural network framework for jointly optimizing three tasks: simile sentence classification, simile component extraction and language modeling. The experimental results show that the neural network based approaches can outperform all rule-based and feature-based baselines. Both simile sentence classification and simile component extraction can benefit from multitask learning. The former can be solved very well, while the latter is more difficult.
Anthology ID:
D18-1183
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1543–1553
Language:
URL:
https://aclanthology.org/D18-1183
DOI:
10.18653/v1/D18-1183
Bibkey:
Cite (ACL):
Lizhen Liu, Xiao Hu, Wei Song, Ruiji Fu, Ting Liu, and Guoping Hu. 2018. Neural Multitask Learning for Simile Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1543–1553, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Neural Multitask Learning for Simile Recognition (Liu et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1183.pdf