基于记忆网络的知识感知医疗对话生成

Memory Networks Based Knowledge-Aware Medical Dialogue Generation

为了解决就医过程中医疗资源短缺和患者时间不充裕、行程不便的问题,提出了结合外部知识的基于记忆网络的知识感知医疗对话生成模型(memory networks based knowledge-aware medical dialogue generation model,MKMed).该模型首先通过利用精确字匹配的方法在对话历史中进行实体追踪;随后在外部实体知识数据库里设计2阶段的实体预测,筛选出可能出现在回复中的医疗实体及对应知识,其中2阶段实体预测分别利用计算共现矩阵和余弦相似度的方法;模型接着用记忆网络来存储知识和对话历史的信息;最后整合记忆网络存储的信息,并使用注意力机制以及循环神经网络生成回复.在带有外部知识的大规模医疗对话数据集KaMed上进行了相关实验,该数据集为收集自在线平台的真实数据.实验结果表明提出的模型生成的回复在流畅性、多样性、正确性和专业性等方面均显著优于大部分基准模型.证明了合理引入外部知识的医疗对话模型能产生成更有医疗价值的回复.

Due to the shortage of medical resources,insufficient time and inconvenient travel,many patients can not get timely diagnosis or treatment.In this paper,a model named MKMed(knowledge-aware memory networks-based medical dialogue generation model)is proposed.It incorporates professional medical external knowledge to generate response.Concretely,the proposed model first tracks knowledge entities from dialogue history by exact word matching.Next,it performs a two-stage prediction in the external knowledge base to select medical entities and corresponding knowledge,which can be used in generating response.The two-stage prediction mainly uses methods for calculating co-occurrence matrix and cosine similarity between entities.Then it uses memory networks to store the information of knowledge and history.Finally,MKMed generates responses incorporating the stored information in the memory networks and uses recurrent neural network with attention mechanism.Experiments are carried out on a large-scale medical dialogue dataset with external knowledge,KaMed,which is a realistic dataset collected from the online platform.The experimental results indicate that the proposed model,MKMed,is significantly superior to the most baselines in terms of fluency,diversity,correctness and professionalism of response generation.This paper reveals that importing external knowledge with rational devised method is helpful for generating more precise and meaningful response.