论文标题
基于加权的异构图对话系统
A Weighted Heterogeneous Graph Based Dialogue System
论文作者
论文摘要
基于知识的对话系统吸引了对不同应用的研究兴趣。但是，对于疾病诊断，由于传统知识图的边缘未加权，因此很难代表症状 - 症状关系和症状疾病关系的关系图。大多数关于疾病诊断对话系统的研究高度依赖于数据驱动的方法和统计特征，缺乏对症状疾病关系关系和症状症状关系的深刻理解。为了解决此问题，这项工作提出了一个基于加权的异质图对话系统，用于疾病诊断。具体而言，我们基于症状同时发生和提出的症状频率疾病频率建立了加权异质图。然后，这项工作提出了一个基于图形的深Q-Network（Graph-DQN），以进行对话管理。通过将图形卷积网络（GCN）与DQN相结合，以从加权异质图中的结构和属性信息中学习疾病和症状的嵌入，Graph-DQN可以更好地捕获症状 - 疾病 - 症状关系和症状 - 症状关系。实验结果表明，所提出的对话系统可与最先进的模型相媲美。更重要的是，提议的对话系统可以通过更少的对话转弯来完成任务，并具有更好的症状疾病能力。
Knowledge based dialogue systems have attracted increasing research interest in diverse applications. However, for disease diagnosis, the widely used knowledge graph is hard to represent the symptom-symptom relations and symptom-disease relations since the edges of traditional knowledge graph are unweighted. Most research on disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of symptom-disease relations and symptom-symptom relations. To tackle this issue, this work presents a weighted heterogeneous graph based dialogue system for disease diagnosis. Specifically, we build a weighted heterogeneous graph based on symptom co-occurrence and a proposed symptom frequency-inverse disease frequency. Then this work proposes a graph based deep Q-network (Graph-DQN) for dialogue management. By combining Graph Convolutional Network (GCN) with DQN to learn the embeddings of diseases and symptoms from both the structural and attribute information in the weighted heterogeneous graph, Graph-DQN could capture the symptom-disease relations and symptom-symptom relations better. Experimental results show that the proposed dialogue system rivals the state-of-the-art models. More importantly, the proposed dialogue system can complete the task with less dialogue turns and possess a better distinguishing capability on diseases with similar symptoms.
