标题：From Stances' Imbalance to Their Hierarchical Representation and Detection
作者：Zhang, Qiang; Liang, Shangsong; Lipani, Aldo; Ren, Zhaochun; Yilmaz, Emine
作者机构：[Zhang, Qiang; Lipani, Aldo; Yilmaz, Emine] UCL, London, England.; [Liang, Shangsong] Sun Yat Sen Univ, Guangzhou, Guangdong, Peoples R China.; [R 更多
会议名称：World Wide Web Conference (WWW)
会议日期：MAY 13-17, 2019
来源：WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019)
关键词：hierarchical classifier; maximum mean discrepancy
摘要：Stance detection has gained increasing interest from the research community due to its importance for fake news detection. The goal of stance detection is to categorize an overall position of a subject towards an object into one of the four classes: agree, disagree, discuss, and unrelated. One of the major problems faced by current machine learning models used for stance detection is caused by a severe class imbalance among these classes. Hence, most models fail to correctly classify instances that fall into minority classes. In this paper, we address this problem by proposing a hierarchical representation of these classes, which combines the agree, disagree, and discuss classes under a new related class. Further, we propose a two-layer neural network that learns from this hierarchical representation and controls the error propagation between the two layers using the Maximum Mean Discrepancy regularizer. Compared with conventional four-way classifiers, this model has two advantages: (1) the hierarchical architecture mitigates the class imbalance problem; (2) the regularization makes the model to better discern between the related and unrelated stances. An extensive experimentation demonstrates state-of-the-art accuracy performance of the proposed model for stance detection.