Hierarchical Hybrid Attention Networks for Chinese Conversation Topic Classification

Yujun Zhou, Changliang Li, Bo Xu, Jiaming Xu, Jie Cao, Bo Xu

Abstract: Topic classification is useful for applications such as forensics analysis and cyber-crime investigation. To improve the overall performance on the task of Chinese conversation topic classification, we propose a hierarchical neural network with automatic semantic features selection, which is a hierarchical architecture that depicts the structure of conversations. The model firstly incorporates speaker information into the character- and word-level attentions and generates sentence representation, then uses attention-based BLSTM to construct the conversation representation. Experimental results on three datasets demonstrate that our model achieves better performance than multiple baselines. It indicates that the proposed architecture can capture the informative and salient features related to the meaning of a conversation for topic classification. And we release the dataset of this paper that can be obtained from https://github.com/njoe9/H-HANs.

External IDs:dblp:conf/iconip/ZhouLXXCX17