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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/P18-2123.pdf
- Code
- nuaaxc/cross_target_stance_classification