M^2-MedDialog: A Dataset and Benchmarks for Multi-domain Multi-service Medical Dialogues

• URL: http://arxiv.org/abs/2109.00430v1
• Date: Wed, 1 Sep 2021 15:24:54 GMT
• Title: M^2-MedDialog: A Dataset and Benchmarks for Multi-domain Multi-service Medical Dialogues
• Authors: Guojun Yan and Jiahuan Pei and Pengjie Ren and Zhumin Chen and Zhaochun Ren and Huasheng Liang
• Abstract summary: Medical dialogue systems (MDSs) aim to assist doctors and patients with a range of professional medical services. No dataset has so large-scale dialogues contains both multiple medical services and fine-grained medical labels. We first build a Multiple-domain Multiple-service medical dialogue (M2-MedDialog)dataset, which contains 1,557 conversations between doctors and patients.
• Score: 25.58066103487436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical dialogue systems (MDSs) aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, consultation, and treatment. However, one-stop MDS is still unexplored because: (1) no dataset has so large-scale dialogues contains both multiple medical services and fine-grained medical labels (i.e., intents, slots, values); (2) no model has addressed a MDS based on multiple-service conversations in a unified framework. In this work, we first build a Multiple-domain Multiple-service medical dialogue (M^2-MedDialog)dataset, which contains 1,557 conversations between doctors and patients, covering 276 types of diseases, 2,468 medical entities, and 3 specialties of medical services. To the best of our knowledge, it is the only medical dialogue dataset that includes both multiple medical services and fine-grained medical labels. Then, we formulate a one-stop MDS as a sequence-to-sequence generation problem. We unify a MDS with causal language modeling and conditional causal language modeling, respectively. Specifically, we employ several pretrained models (i.e., BERT-WWM, BERT-MED, GPT2, and MT5) and their variants to get benchmarks on M^2-MedDialog dataset. We also propose pseudo labeling and natural perturbation methods to expand M2-MedDialog dataset and enhance the state-of-the-art pretrained models. We demonstrate the results achieved by the benchmarks so far through extensive experiments on M2-MedDialog. We release the dataset, the code, as well as the evaluation scripts to facilitate future research in this important research direction.

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