Neural Stance Detection With Hierarchical Linguistic Representations
Zhongqing Wang, Qingying Sun, Shoushan Li, Qiaoming Zhu, Guodong Zhou
Abstract
Stance detection aims to assign a stance label (i.e., favor or against) to a post towards a specific target. Recently, there is a growing interest in adopting neural models to detect stance of a document. However, most of these works focus on modeling the sequence of words to learn document representation, though other linguistic information, such as sentiment and arguments, are correlated with the stance of document, and may inspire us to explore the stance. In this article, we propose a hierarchical attention neural model to well study various linguistic information to better represent a document via hierarchical linguistic representations. In addition, we propose a hierarchical network with attention mechanism to weight the importance of various kinds of linguistic information, and learn the mutual attention between document and linguistic information. Detail evaluation on two benchmark datasets demonstrates the effectiveness of proposed hierarchical network with attention mechanism.