  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.
