Knowledge-Enhanced Interactive Matching Network for Multi-turn Response Selection in Medical Dialogue Systems

Recently, the response selection for retrieval-based dialogue systems has gained enormous attention from both academic and industrial communities. Although the previous methods achieve promising results for intelligent customer service systems and open-domain chatbots, the response selection in medical dialogues suffers from lower performance because of the strong dependency on the domain knowledge. In this paper, we construct two specialized medical knowledge bases and propose a Knowledge-enhanced Interactive Matching Network (KIMN) for multi-turn response selection in medical dialogue systems. Compared with previous response selection approaches, the KIMN adopts pre-trained language model to alleviate the limited training data problem, and incorporates internal and external medical domain knowledge to perform interactive matching between responses and contexts. The experiments on a real-world medical dialogue dataset show that our proposed model consistently outperforms the strong baseline methods by large margins, which shows that our proposed model can retrieve more accurate responses for medical dialogue systems.