Attention and Graph Matching Network for Retrieval-Based Dialogue System with Domain Knowledge

Abstract

Building an effective and friendly human-machine dialogue system is one of the major challenges in Artificial Intelligence. This work proposes a new model named Graph and Attention Matching Network (AGMN) for response selection in retrieval-based dialogue system. AGMN model consists of two parts: cross attention mechanism and knowledge representation extractor. Specifically, the cross attention mechanism is exploited to obtain the dual representation from context and response words because these representations can provide the useful matching information for determining whether the next utterance is suitable response or not. Besides, the domain knowledge relationships which are extracted from Linux manuals are incorporated into the word representation by graph attention mechanism. Experimental results on Ubuntu Dialogue Corpus showed that both cross attention mechanism and domain knowledge can contribute to the performance of response selection and the AGMN model proposed in this paper outperforms the state-of-art approaches.