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 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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-ofthe-art performance in the ZSSD task 1 .