  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.
