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Using natural language processing (NLP) technologies to develop medicalchatbots makes the diagnosis of the patient more convenient and efficient,which is a typical application in healthcare AI. Because of its importance,lots of research have been come out. Recently, the neural generative modelshave shown their impressive ability as the core of chatbot, while it cannotscale well when directly applied to medical conversation due to the lack ofmedical-specific knowledge. To address the limitation, a scalable MedicalKnowledge Assisted mechanism, MKA, is proposed in this paper. The mechanismaims to assist general neural generative models to achieve better performanceon the medical conversation task. The medical-specific knowledge graph isdesigned within the mechanism, which contains 6 types of medical-relatedinformation, including department, drug, check, symptom, disease, food.Besides, the specific token concatenation policy is defined to effectivelyinject medical information into the input data. Evaluation of our method iscarried out on two typical medical datasets, MedDG and MedDialog-CN. Theevaluation results demonstrate that models combined with our mechanismoutperform original methods in multiple automatic evaluation metrics. Besides,MKA-Bert-GPT achieves state-of-the-art performance. The open-sourced codes arepublic:https://github.com/LIANGKE23/Knowledge_Assisted_Medical_Dialogue_Generation_Mechanism

MKA: A Scalable Medical Knowledge Assisted Mechanism for Generative Models on Medical Conversation Tasks