Medical Dialogue Generation (MDG) is intended to build a medical dialogue
system for intelligent consultation, which can communicate with patients in
real-time, thereby improving the efficiency of clinical diagnosis with broad
application prospects. This paper presents our proposed framework for the
Chinese MDG organized by the 2021 China conference on knowledge graph and
semantic computing (CCKS) competition, which requires generating
context-consistent and medically meaningful responses conditioned on the
dialogue history. In our framework, we propose a pipeline system composed of
entity prediction and entity-aware dialogue generation, by adding predicted
entities to the dialogue model with a fusion mechanism, thereby utilizing
information from different sources. At the decoding stage, we propose a new
decoding mechanism named Entity-revised Diverse Beam Search (EDBS) to improve
entity correctness and promote the length and quality of the final response.
The proposed method wins both the CCKS and the International Conference on
Learning Representations (ICLR) 2021 Workshop Machine Learning for Preventing
and Combating Pandemics (MLPCP) Track 1 Entity-aware MED competitions, which
demonstrate the practicality and effectiveness of our method.

本研究提出了一个用于智能咨询的医疗对话系统，该系统能够与患者进行实时交流，从而提高临床诊断的效率，并实现了中文医疗对话生成的竞赛，提出了一种由实体预测和实体感知式对话生成组成的流水线系统，并使用融合机制将预测的实体添加到对话模型中，在解码阶段，我们提出了一种新的解码机制 Entity-revised Diverse Beam Search（EDBS），以提高实体的正确性以及生成的最终响应长度和质量。提出的方法赢得了 CCKS 和 MLPCP 举办的两项国际医疗比赛，充分证明了我们方法的实用性和有效性。