Zero-shot stance detection based on multi-perspective transferable feature fusion

Zero-shot stance detection involves predicting stances that have not previously been encountered by adapting models to learn transferable features by aligning the source and destination target spaces. The acquisition of transferable target-invariant features is crucial for zero-shot stance detection. This work proposes a stance detection technique that can effectively adapt to new unseen targets, and the essence lies in acquiring fine-grained and easy-to-migrate target-invariant features from multiple perspectives as transferable knowledge. Specifically, we first perform data augmentation by masking topic keywords to mitigate the target dependency introduced by topic keywords in the text. Then, to account for the diversity and granularity of the sample features, we leverage instance-wise contrastive learning to extract transferable meta-features from multiple perspectives. The meta-features bridge features migration from known targets to unseen targets by incorporating different viewpoints. Finally, we incorporate an attention mechanism to fuse the multi-perspective transferable features for predicting the stance of previously unseen targets. The experimental results demonstrate the superiority of our model over competitive baselines across four benchmark datasets.