基于双流建模的医疗对话生成
Medical Dialogue Generation via Dual Flow Modeling
AI总结：
本文提出DFMed框架，通过建模医疗实体转移和医生对话行为的双流交互，提升医疗对话生成质量，并在两个数据集上验证了其优越性。
摘要：
医疗对话系统（MDS）旨在为患者提供诊断和处方等医疗服务。由于大多数患者无法精确描述自身症状，对话理解对MDS而言具有挑战性。以往研究主要通过提取提及的医疗实体作为关键对话历史信息来解决这一问题。在本工作中，我们认为同样至关重要的是捕捉每一轮中医疗实体的转移以及医生的对话行为，因为它们有助于理解对话如何流动，并增强对下一轮将要采用的实体和对话行为的预测。相应地，我们提出了一个双流增强医疗（DFMed）对话生成框架。该框架提取对话历史中使用的医疗实体和对话行为，并分别通过以实体为中心的图流和顺序行为流对其转移进行建模。我们采用两个序列模型对其进行编码，并设计了一个交织组件来增强它们之间的交互。在两个数据集上的实验表明，我们的方法在自动评估和人工评估中均超越了基线。
英文摘要：
Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. Since most patients cannot precisely describe their symptoms, dialogue understanding is challenging for MDS. Previous studies mainly addressed this by extracting the mentioned medical entities as critical dialogue history information. In this work, we argue that it is also essential to capture the transitions of the medical entities and the doctor's dialogue acts in each turn, as they help the understanding of how the dialogue flows and enhance the prediction of the entities and dialogue acts to be adopted in the following turn. Correspondingly, we propose a Dual Flow enhanced Medical (DFMed) dialogue generation framework. It extracts the medical entities and dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow, respectively. We employ two sequential models to encode them and devise an interweaving component to enhance their interactions. Experiments on two datasets demonstrate that our method exceeds baselines in both automatic and manual evaluations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
