Prompt Learning-based Generative Approach Towards Medical Dialogue Understanding
The goal of the dialogue understanding module in task-oriented dialogue systems is to convert the user's natural lan-guage input into a structured form.However,in the diagnosis-oriented medical dialogue system,the existing approaches face the following problems:1)the granularity of the information cannot fully satisfy the needs of diagnosis,such as providing the severity of a symptom;2)it is difficult to simultaneously satisfy the diverse representations of slot values in the medical domain,such as"symptom",which may contain non-contiguous and nested entities,and"negation",which may contain categorical value.This pa-per proposes a generative medical dialogue understanding method based on prompt learning.To address problem 1),this paper re-places the single-level slot structure in the current dialogue understanding task with a multi-level slot structure to represent finer-grained information,and then proposes a generative approach based on dialogue-style prompts,which uses prompt tokens to simu-late the dialogue between doctor and patient and obtain multi-level information from multiple rounds of interaction.To address problem 2),this paper proposes the use of a restricted decoding strategy in the inference process,so that the model can handle the intention detection and slot-filling tasks of extractive and categorical slots in a unified manner to avoid complex modeling.In addi-tion,to address the problem of lacking labeled data in the medical domain,this paper proposes a two-stage training strategy to le-verage the large-scale unlabeled medical dialogue corpus to improve performance.In this paper,a dataset containing 4 722 dia-logues involving 17 intentions and 74 types of slots is annotated and published for the medical dialogue understanding task contai-ning a multi-level slot structure.Experiment shows that the proposed approach can effectively parse various complex entities in medical dialogues,with 2.18％higher performance compared to existing generation methods.The two-stage training can improve the performance of the model by up to 5.23％in the scenario with little data.
Prompt learningNatural language understandingMedical dialogue systemGenerative modelTwo-stage training
万方数据
维普
