Knowledge-enhanced Multi-Granularity Interaction Network for Political Perspective Detection

Xinming Chen, Xu Liu, Yuanxing Xu, Bin Wu, Yangfu Zhu

Abstract: Nowadays, with political ideologies becoming increasingly polarized, detecting political perspectives has become more and more crucial. Previous studies mainly focused on reasoning with real-world entities as background knowledge, while they fail to effectively model hierarchical relationships in the text structure and leave out the document-level knowledge such as topics in a news article. To overcome these limitations, we propose KMGN, a novel Knowledge-enhanced Multi-Granularity Interaction Network for political perspective prediction which consists of two key components: (1) a knowledge infusion method for learning both the topic representations and political entities of a news article (2) a multi-granularity interaction network to learn semantic representations for words, sentences, and document and integrate the infused knowledge across different granularities. Extensive experiments shows that our proposed approach is effective in predicting the political stance in a news article. An ablation study demonstrates the contribution of each component in our model.