J oint CL : A Joint Contrastive Learning Framework for Zero-Shot Stance Detection (2022)

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.

## introduction stance detection aims to automatically identify one's opinionated standpoint/attitude (e.g. pro, con, or neutral, etc.) expressed in text towards a specific proposition, topic, or target @xcite @xcite . for example, a text "everyone is able to believe in whatever they want." expresses a stance of "pro" towards the target "atheism". existing methods achieved promising performance in in-target stance detection when trained and tested on the datasets towards the same set of * equal contribution † corresponding author 1 the source code of this work is released at https:// github.com/hitsz-hlt/jointcl targets @xcite , and in cross-target stance detection that identifies the stance of a destination target using models trained on a related source target in a one-to-one way @xcite @xcite . in practice, however, it is infeasible to enumerate all possible (in-target) or related (cross-target) targets beforehand for training stance detection models. hence, zero-shot stance detection (zssd) @xcite , which aims to detect the stance for unseen targets during the inference stage is a promising scenario forward. to deal with zssd, intuitively, we can either reason the target-based stance features from the learned stance information based on the context (i.e., from the context-aware perspective), or identify stance information that is potentially relevant with unseen targets from the learned target-related stance expressions (i.e., from the target-aware perspective). existing research attempted to explore attention mechanism @xcite , adversarial learning @xcite , or graph architecture based on external commonsense knowledge @xcite to learn the stance representations from the context regarding the known targets, aiming to generalize the learned stance features to the unseen targets for zssd. but they tend to ignore that the stance information of an unseen target can be represented in the light of the known targets from the target-aware perspective. in this paper, to generalize the stance features to the unseen targets, we propose a joint contrastive learning (jointcl) framework to leverage the stance features of known targets from both the context-aware and the target-aware perspectives. on the one hand, from the context-aware perspective, we explore a stance contrastive learning strategy, which effectively improves the quality of stance features by leveraging the similarity of training instances in a stance class while pushing away instances from other stance classes. this essentially allows the exploitation of target-based contextual stance features to better generalize to the unseen targets. on the other hand, from the targetaware perspective, we propose a feasible solution to capture the relationships between the known targets and the unseen ones. specifically, inspired by @xcite , we explore a clustering method to generate prototypes from all training instances. we then build prototypical graphs linking the prototypes with the target-based representations, in which each prototype is regarded as a bridge that allows the sharing of the graph structures between known targets and unseen ones. based on the prototypical graphs, we devise a novel target-aware prototypical graph contrastive learning strategy to learn the correlation and difference among the target-based representations. specifically, a novel edge-oriented graph contrastive loss is deployed to make the graph structures similar for similar target-based representations, and different for dissimilar ones. this essentially generalizes the graph structures learned from the known targets to the unseen ones, so as to better derive target-aware stance information for the unseen targets by the graph representations. the main contributions of our work are summarized as follows: ## methodology in this section, we describe the proposed joint contrastive learning (jointcl) framework for zeroshot stance detection in detail. as demonstrated in figure 1 , the architecture of the jointcl framework contains four main components: 1) stance contrastive learning, which performs contrastive learning based on the supervised signal of stance labels for better generalization of stance features; 2) prototypes generation, which derives the prototypes of the training data by a clustering method; 3) target-aware prototypical graph contrastive learning, which performs the edge-oriented graph contrastive learning strategy based on the target-aware prototypical graphs for sharing the graph structures between known targets and unseen ones; 4) classifier, which detects the stances of targets based on the hidden vectors and graph representations.