Terminology-aware Medical Dialogue Generation

Chen Tang; Hongbo Zhang; Tyler Loakman; Chenghua Lin; Frank Guerin

doi:10.48550/arxiv.2210.15551

Back

Preprint

Terminology-aware Medical Dialogue Generation

Chen Tang, Hongbo Zhang, Tyler Loakman, Chenghua Lin and Frank Guerin

arXiv (Cornell University)

27/10/2022

DOI: https://doi.org/10.48550/arxiv.2210.15551

Abstract

Computer Science - Computation and Language

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.

Metrics

6 Record Views

Details

Title: Terminology-aware Medical Dialogue Generation
Creators: Chen Tang - University of Surrey, School of Computer Science and Electronic Engineering
Hongbo Zhang
Tyler Loakman
Chenghua Lin
Frank Guerin - University of Surrey, School of Computer Science and Electronic Engineering
Publication Details: arXiv (Cornell University)
Grant note: The authors acknowledge the support of China Scholarship Council (CSC) (File No.202006120039) and UK Research and Innovation [grant number EP/S023062/1].
Identifiers: 99817636302346
Copyright: The URI http://arxiv.org/licenses/nonexclusive-distrib/1.0/ is used to record the fact that the submitter granted the following license to arXiv.org on submission of an article: I grant arXiv.org a perpetual, non-exclusive license to distribute this article. I certify that I have the right to grant this license. I understand that submissions cannot be completely removed once accepted. I understand that arXiv.org reserves the right to reclassify or reject any submission.
Academic Unit: School of Computer Science and Electronic Engineering
Language: English
Resource Type: Preprint

Terminology-aware Medical Dialogue Generation

Abstract

Metrics

Details

Usage Policy