论文标题
术语感知的医学对话世代
Terminology-aware Medical Dialogue Generation
论文作者
论文摘要
医学对话的生成旨在根据医生和患者之间的对话史产生反应。与开放域对话的生成不同，这需要特定于医疗领域的背景知识。现有的医学对话生成框架无法纳入特定领域的知识，尤其是在医学术语方面。在本文中，我们提出了一个新颖的框架，以考虑以域特异性术语为中心的特征来改善医学对话的产生。我们利用一种注意机制来纳入术语中心的特征，并通过强制执行语言模型来学习具有辅助术语识别任务的术语表示，并填补医学背景知识和常见话语之间的语义差距。实验结果证明了我们的方法的有效性，在该方法中，我们提议的框架的表现优于SOTA语言模型。此外，我们还提供了一个新的数据集，其中包含医学术语注释，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上找到。
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.
