Cross-Target Stance Classification with Self-Attention Networks

Chang Xu, Cécile Paris, Surya Nepal, Ross Sparks


Abstract
In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this work, we explore the potential for generalizing classifiers between different targets, and propose a neural model that can apply what has been learned from a source target to a destination target. We show that our model can find useful information shared between relevant targets which improves generalization in certain scenarios.
Anthology ID:
P18-2123
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
778–783
Language:
URL:
https://aclanthology.org/P18-2123
DOI:
10.18653/v1/P18-2123
Bibkey:
Cite (ACL):
Chang Xu, Cécile Paris, Surya Nepal, and Ross Sparks. 2018. Cross-Target Stance Classification with Self-Attention Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 778–783, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Cross-Target Stance Classification with Self-Attention Networks (Xu et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2123.pdf
Presentation:
 P18-2123.Presentation.pdf
Video:
 https://aclanthology.org/P18-2123.mp4
Code
 nuaaxc/cross_target_stance_classification