Distinct but correct: generating diversified and entity-revised medical response

Medical dialogue generation (MDG) is applied for building medical dialogue systems for intelligent consultation. Such systems can communicate with patients in real time, thereby improving the efficiency of clinical diagnosis. However, predicting correct entities and correctly generating distinct responses remain a great challenge. Inspired by actual doctors’ responses to patients, we consider MDG a two-stage task: entity prediction and dialogue generation. For entity prediction, we design an ent-mac post pre-training strategy by leveraging external medical entity knowledge to enhance the pre-trained model. For dialogue generation, we propose an entity-aware fusion MDG method in which predicted entities are integrated into the dialogue generation model through different encoding fusion mechanisms, using information from different sources. Because the diverse beam search algorithm can produce responses with entities that deviate from the predicted entities, an entity-revised diverse beam search is proposed to correct the entities entailed in the generated responses and make the generated responses more distinct. The experimental results on the China Conference on Knowledge Graph and Semantic Computing 2021 (A/B tests) and the International Conference on Learning Representations 2021 (online test) datasets show that the proposed method outperforms several state-of-the-art methods, which demonstrates its practicability and effectiveness.