Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph

In this paper, we consider a realistic scenario on stance detection with more application potential, i.e., zero-shot and few-shot stance detection, which identifies stances for a wide range of topics with no or very few training examples. Conventional data-driven approaches are not applicable to the above zero-shot and few-shot scenarios. For human beings, commonsense knowledge is a crucial element of understanding and reasoning. In the absence of annotated data and cryptic expression of users' stance, we believe that introducing commonsense relational knowledge as support for reasoning can further improve the generalization and reasoning ability of the model in the zero-shot and few-shot scenarios. Specifically, we introduce a commonsense knowledge enhanced model to exploit both the structurallevel and semantic-level information of the relational knowledge. Extensive experiments demonstrate that our model outperforms the state-of-the-art methods on zero-shot and fewshot stance detection task.