“In recent years, many researchers have recognized the importance of associating events withsentiments. Previous approaches focus on generalizing events and extracting sentimental in-formation from a large-scale corpus. However, since context is absent and sentiment is oftenimplicit in the event, these methods are limited in comprehending the semantics of the eventand capturing effective sentimental clues. In this work, we propose a novel Multi-perspectiveKnowledge-injected Interaction Network (MKIN) to fully understand the event and accuratelypredict its sentiment by injecting multi-perspective knowledge. Specifically, we leverage con-texts to provide sufficient semantic information and perform context modeling to capture thesemantic relationships between events and contexts. Moreover, we also introduce human emo-tional feedback and sentiment-related concepts to provide explicit sentimental clues from theperspective of human emotional state and word meaning, filling the reasoning gap in the senti-ment prediction process. Experimental results on the gold standard dataset show that our modelachieves better performance over the baseline models.”

Dataset bias in stance detection tasks allows models to achieve superior performance without using targets. Most existing debiasing methods are task-agnostic, which fail to utilize task knowledge to better discriminate between genuine and bias features. Motivated by how humans tackle stance detection tasks, we propose to incorporate the stance reasoning process as task knowledge to assist in learning genuine features and reducing reliance on bias features. The full stance reasoning process usually involves identifying the span of the mentioned target and corresponding opinion expressions, such fine-grained annotations are hard and expensive to obtain. To alleviate this, we simplify the stance reasoning process to relax the granularity of annotations from token-level to sentence-level, where labels for sub-tasks can be easily inferred from existing resources. We further implement those sub-tasks by maximizing mutual information between the texts and the opinioned targets. To evaluate whether stance detection models truly understand the task from various aspects, we collect and construct a series of new test sets. Our proposed model achieves better performance than previous task-agnostic debiasing methods on most of those new test sets while maintaining comparable performances to existing stance detection models.