Medical dialogue systems may converse with patients and make a diagnosis automatically. Conventional dialogue generation models cannot be directly applied to such scenarios because of the lack of medical knowledge. A recent study addresses the lack of suitable models in this domain and introduces an end-to-end dialogue system for the medical dialogue generation.
Firstly, the conversation is encoded into hierarchical representations. A meta-knowledge graph reasoning network characterizes the correlations among diseases and symptoms, which evolve with new context information. Finally, a response to the requested symptoms is generated.
Moreover, a novel graph-evolving meta-learning framework