Infusing Knowledge from W ikipedia to Enhance Stance Detection (2022)

Abstract: Stance detection infers a text author’s attitude towards a target. This is challenging when the model lacks background knowledge about the target. Here, we show how background knowledge from Wikipedia can help enhance the performance on stance detection. We introduce Wikipedia Stance Detection BERT (WS-BERT) that infuses the knowledge into stance encoding. Extensive results on three benchmark datasets covering social media discussions and online debates indicate that our model significantly outperforms the state-of-the-art methods on target-specific stance detection, cross-target stance detection, and zero/few-shot stance detection.

## introduction stance detection aims to automatically identify author's attitude or standpoint (favor, neutral, against) towards a specific target or topic using text as evidence @xcite @xcite @xcite . to precisely capture the stance towards a target, background knowledge about the target is often necessary, especially in cases where the text does not explicitly mention the target, as shown in figure 1 . people have wide-ranging background knowledge regarding various targets and use it to infer the implicit stance in a statement. however, machines by default do not have such knowledge and previous works on stance detection @xcite @xcite @xcite @xcite fail to incorporate such knowledge in modeling stances. in this paper, we propose to utilize background knowledge from wikipedia about the target as a target: donald trump stance: favor document: all republican presidents are better! background knowledge required: donald trump was a republican president. ## conclusion in this paper we propose to utilize background knowledge about targets from wikipedia to enhance stance detection. we propose ws-bert with two variants to encode such knowledge. such a simple yet effective method achieves state-of-theart performance on three benchmark datasets and on three sub-tasks: in-target stance detection, crosstarget stance detection, and zero/few-shot stance detection. the comprehensive and growing list of topics covered by wikipedia ensures that our method will adapt to newly emerging targets. in the future, we plan to investigate incorporating knowledge about entities in the input documents, in addition to knowledge about the targets. since wikipedia pages may contain subjective opinions towards the targets, how to prevent the model from being negatively impacted by such bias when modeling the knowledge remains a promising research direction. moreover, background knowledge from relevant news articles might also be helpful for inferring stances.