Improving Medical Dialogue Generation with Abstract Meaning Representations

TL;DR

This paper introduces a novel medical dialogue generation framework that leverages Abstract Meaning Representations (AMR) graphs to better capture semantic and medical knowledge, significantly improving generation quality.

Contribution

The paper proposes a new AMR-based graphical modeling approach for medical dialogues, integrating textual and graphical knowledge with a dual attention mechanism.

Findings

Outperforms baseline models in medical dialogue generation

Demonstrates the effectiveness of AMR graphs in capturing medical semantics

Provides source code for future research

Abstract

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.