Proceedings of the Annual Meeting of the Cognitive Science Society

Stance detection for unseen targets is designed to automatically identify the user's stance or attitude towards various new targets that are constantly appearing with no labels. Inspired by work in cognitive science, we distinguish functions between systems for syntactic and semantic to enhance stance detection. First, we construct a dual-view graph and utilize unsupervised graph contrastive learning to capture target-invariant features influencing stance expression from a syntactic structure perspective. Second, we use an attention mechanism to learn the relationship between syntactic pattern features and a given target, and fuse the two parts to enhance the model's ability to predict unseen targets. Meanwhile, we employ the interactive GCN to maintain the global semantics of the dual-view graph fusion and ensure the stability and validity of the learned syntactic representations. Comprehensive experiments on stance detection of unseen targets verify the effectiveness and superiority of our proposed method.