Medical dialogue generation aims to generate responses according to a history of dialogue turns between doctors and patients. Unlike open-domain dialogue generation, this requires background knowledge specific to the medical domain. Existing generative frameworks for medical dialogue generation fall short of incorporating domain-specific knowledge, especially with regard to medical terminology. In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology. We leverage an attention mechanism to incorporate terminologically centred features, and fill in the semantic gap between medical background knowledge and common utterances by enforcing language models to learn terminology representations with an auxiliary terminology recognition task. Experimental results demonstrate the effectiveness of our approach, in which our proposed framework outperforms SOTA language models. Additionally, we provide a new dataset with medical terminology annotations to support the research on medical dialogue generation. Our dataset and code are available at https://github.com/tangg555/meddialog.

翻译：医疗对话生成旨在根据医生与患者之间的对话历史生成回复。与开放域对话生成不同，这需要特定于医学领域的背景知识。现有的医疗对话生成框架在整合领域特定知识方面存在不足，尤其是针对医学术语的处理。本文提出了一种新颖框架，通过考虑以领域特定术语为核心的特征来改进医疗对话生成。我们利用注意力机制整合以术语为中心的特征，并通过强制语言模型学习术语表示（辅助术语识别任务），填补医学背景知识与常见表述之间的语义鸿沟。实验结果表明了该方法的有效性，所提框架优于当前最优语言模型。此外，我们提供了一个带有医学术语标注的新数据集，以支持医疗对话生成研究。数据集与代码可在 https://github.com/tangg555/meddialog 获取。