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

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

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Medical dialogue generation(MDG)is applied for building medical dialogue systems for intelli-gent 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 genera-tion,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 Confer-ence 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.

medical entity predictionent-mac post pre-training strategyentity-aware fusion medical dia-logue generationencoding fusion mechanismentity-revised diverse beam search

Bin LI、Bin SUN、Shutao LI、Encheng CHEN、Hongru LIU、Yixuan WENG、Yongping BAI、Meiling HU

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College of Electrical and Information Engineering,Hunan University,Changsha 410082,China

School of Mathematics,Sun Yat-sen University,Guangzhou 510275,China

JD Technology,Beijing 101100,China

National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China

Xiangya Hospital of Central South University,Changsha 410008,China

Teaching and Research Section of Clinical Nursing,Xiangya Hospital of Central South University,Changsha 410008,China

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国家重点研发计划国家自然科学基金Project of Hunan Provincial Health Commission

2018YFB130520062171183202114010841

2024

10.1007/s11432-021-3534-9

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(3)
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