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Heterogeneous graph reasoning for knowledge-grounded medical dialogue system

机译:知识接地医学对话系统的异质图推理

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摘要

Beyond the common difficulties faced in task-oriented dialogue system, medical dialogue has recently attracted increasing attention due to its huge application potential while posing more challenges in reasoning over medical domain knowledge and logic. Existing works resort to neural language models for dialogue embedding and neglect the explicit logical reasoning, leading to poor explainable and generalization ability. In this work, we propose an explainable Heterogeneous Graph Reasoning (HGR) model to unify the relational dialogue context understanding and entity-correlation reasoning into a heterogeneous graph structure. HGR encodes entity context according to the corresponding utterance and deduces next response after fusing the underlying medical knowledge with entity context by attentional graph propagation. To push forward the future research on expert-sensitive task-oriented dialogue system, we first release a large-scale Medical Dialogue Consultant benchmark (MDG-C) with 16 Gastrointestinal diseases for evaluating consultant capability and a Medical Dialogue Diagnosis benchmark (MDG-D) with 6 diseases for measuring diagnosis capability of models, respectively. Extensive experiments on both MDG-C and MDG-D benchmarks demonstrate the superiority of our HGR over state-of-the-art knowledge grounded approaches in general fields of medical dialogue system.(c) 2021 Elsevier B.V. All rights reserved.
机译:除了面临的面向任务的对话系统的共同的困难,医疗对话最近引起越来越多的关注,由于其巨大的应用潜力,而在推理在医疗领域的知识和逻辑构成更大的挑战。现有工程求助于神经语言模型进行对话嵌入和忽视了明确的逻辑推理,导致解释的穷人和推广能力。在这项工作中,我们提出了一个解释的异构图形推理(HGR)模式,以统一的关系对话的上下文的理解和实体相关的推理到异构图形结构。根据由注意力图形传播熔合与实体上下文底层医学知识之后的对应的发声并推导出下一个响应HGR编码实体上下文。推进专家敏感的面向任务的对话系统今后的研究中,我们先用16种胃肠道疾病释放大型医疗对话顾问基准(MDG-C),用于评估顾问能力和医疗对话诊断基准(MDG-d )与6周的疾病,用于分别测量的模型诊断能力。既MDG-C和千年发展目标-d的基准上大量的实验证明了我们在HGR医疗对话系统的一般领域的国家的最先进的知识接地方式的优越性。版权所有(C)2021爱思唯尔B.V.所有权利。

著录项

  • 来源
    《Neurocomputing》 |2021年第28期|260-268|共9页
  • 作者单位

    Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510006 Peoples R China;

    Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510006 Peoples R China;

    Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510006 Peoples R China;

    Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510006 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Deep learning; Dialogue system; Graph reasoning;

    机译:深入学习;对话系统;图推理;

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