  Medical Dialogue Generation serves a critical role in telemedicine by
facilitating the dissemination of medical expertise to patients. Existing
studies focus on incorporating textual representations, which have limited
their ability to represent the semantics of text, such as ignoring important
medical entities. To enhance the model's understanding of the textual semantics
and the medical knowledge including entities and relations, we introduce the
use of Abstract Meaning Representations (AMR) to construct graphical
representations that delineate the roles of language constituents and medical
entities within the dialogues. In this paper, We propose a novel framework that
models dialogues between patients and healthcare professionals using AMR
graphs, where the neural networks incorporate textual and graphical knowledge
with a dual attention mechanism. Experimental results show that our framework
outperforms strong baseline models in medical dialogue generation,
demonstrating the effectiveness of AMR graphs in enhancing the representations
of medical knowledge and logical relationships. Furthermore, to support future
research in this domain, we provide the corresponding source code at
https://github.com/Bernard-Yang/MedDiaAMR.
