Adjacency Pairs-Aware Hierarchical Attention Networks for Dialogue Intent Classification

Jiabao Xu, Peijie Huang, Youming Peng, Jiande Ding, Boxi Huang, Simin Huang

Abstract: Dialogue intent classification is a fundamental and essential task in dialogue systems. Although sentence-level and document-level text classification have made dramatic progress in recent years with the help of deep learning technology, dialogue-level classification remains challenging. Dialogue has unique characteristics that distinguish it from other types of text. Dialogue is interactive, with feedback between speakers, and turn-taking. These unique features suggest that model architecture should take dialogue structure into account to learn a better representation. In this paper we propose an Adjacency Pairs-Aware Hierarchical Attention Network (AP-HAN) for dialogue intent classification. A dialogue reconstruction strategy is designed to match the question and answer utterances properly and then make the dialogue to be presented as a sequence of adjacent pairs. Then, the adjacency pairs features are incorporated into the hierarchical attention network. Experimental results on public CCL2018-Task1 corpus show the better performance of the proposed model.