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.

本研究提出了一种 Dual Flow 增强型医疗对话生成框架 DFMed，通过提取对话历史中的医疗实体和对话行为来预测下一轮中要采取的实体和对话行为，模拟它们之间的转换，并使用两个顺序模型进行编码，运用交织组件增强协同作用，实验结果表明 DFMed 方法在自动评估和手动评估中均优于基线。