Hierarchical Interactive Matching Network for Multi-turn Response Selection in Retrieval-Based Chatbots

Ting Yang, Ruifang He, Longbiao Wang, Xiangyu Zhao, Jiangwu Dang

Abstract: We study multi-turn response selection in open domain dialogue systems, where the best-matched response is selected according to a conversation context. The widely used sequential matching models match a response candidate with each utterance in the conversation context through a representation-interaction-aggregation framework, but do not pay enough attention to the inter-utterance dependencies at the representation stage and global information guidance at the interaction stage. They may lead to the result that the matching features of utterance-response pairs may be one-sided or even noisy. In this paper, we propose a hierarchical interactive matching network (HIMN) to model both aspects in a unified framework. In HIMN, we model the dependencies between adjacency utterances in the context with multi-level attention mechanism. Then a two-level hierarchical interactive matching is exploited to introduce the global context information to assist in distilling important matching features of each utterance-response pair at the interaction stage. Finally, the two-level matching features are merged through gate mechanism. Empirical results on both Douban Corpus and Ecommerce Corpus show that HIMN can significantly outperform the competitive baseline models for multi-turn response selection.