A MULTI DOMAIN KNOWLEDGE ENHANCED MATCHING NETWORK FOR RESPONSE SELECTION IN RETRIEVAL-BASED DIALOGUE SYSTEMS

Building a human-machine conversational agent is a core problem in Artificial Intelligence, where knowledge has to be integrated into the model effectively. In this paper, we propose a Multi Domain Knowledge Enhanced Matching Network (MDKEMN) to build retrieval-based dialogue systems that could leverage both explicit knowledge graph and implicit domain knowledge for response selection. Specifically, our MDKEMN leverages the self-attention mechanism of a single-stream Transformer to make deep interactions among the dialogue context, response candidate and external knowledge graph, and finally returns the matching degree of each context-response pair under the external knowledge. Furthermore, to leverage the implicit domain knowledge from all domains to improve the performance of each domain, we combine the multi-domain datasets for training and then finetune the pretrained model on each domain. Experimental results show (1) the effectiveness of both explicit and implicit knowledge incorporating and (2) the superiority of our approach over previous baselines on a Chinese multi-domain knowledge-driven dialogue dataset.