JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection

Bin Liang, Qinglin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, Ruifeng Xu


Abstract
Zero-shot stance detection (ZSSD) aims to detect the stance for an unseen target during the inference stage. In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning. Specifically, a stance contrastive learning strategy is employed to better generalize stance features for unseen targets. Further, we build a prototypical graph for each instance to learn the target-based representation, in which the prototypes are deployed as a bridge to share the graph structures between the known targets and the unseen ones. Then a novel target-aware prototypical graph contrastive learning strategy is devised to generalize the reasoning ability of target-based stance representations to the unseen targets. Extensive experiments on three benchmark datasets show that the proposed approach achieves state-of-the-art performance in the ZSSD task.
Anthology ID:
2022.acl-long.7
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
81–91
Language:
URL:
https://aclanthology.org/2022.acl-long.7
DOI:
10.18653/v1/2022.acl-long.7
Bibkey:
Cite (ACL):
Bin Liang, Qinglin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, and Ruifeng Xu. 2022. JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 81–91, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection (Liang et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.7.pdf
Software:
 2022.acl-long.7.software.zip
Code
 hitsz-hlt/jointcl