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：医疗对话的产生旨在根据医生和病人之间对话的历史产生回应。与开放域对话的产生不同,这需要医学领域特有的背景知识。医疗对话的现有基因框架不足以纳入具体领域的知识,特别是医学术语方面的知识。在本文件中,我们提出一个新的框架,通过考虑以具体领域术语为中心的特征来改进医疗对话的产生。我们利用一种关注机制,纳入以术语为中心的特征,并通过实施语言模型学习术语表述和辅助术语识别任务来填补医学背景知识与常见语句之间的语义差距。实验结果显示了我们的方法的有效性,其中我们提议的框架优于SOTA语言模式。此外,我们提供了一套带有医学术语说明的新数据集,以支持医学对话生成的研究。我们的数据集和代码可在https://github.com/tangg555/meddialog查阅。