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Medical dialogue systems (MDSs) aim to assist doctors and patients with arange of professional medical services, i.e., diagnosis, treatment andconsultation. The development of MDSs is hindered because of a lack ofresources. In particular. (1) there is no dataset with large-scale medicaldialogues that covers multiple medical services and contains fine-grainedmedical labels (i.e., intents, actions, slots, values), and (2) there is no setof established benchmarks for MDSs for multi-domain, multi-service medicaldialogues. In this paper, we present ReMeDi, a set of resource for medicaldialogues. ReMeDi consists of two parts, the ReMeDi dataset and the ReMeDibenchmarks. The ReMeDi dataset contains 96,965 conversations between doctorsand patients, including 1,557 conversations with fine-gained labels. It covers843 types of diseases, 5,228 medical entities, and 3 specialties of medicalservices across 40 domains. To the best of our knowledge, the ReMeDi dataset isthe only medical dialogue dataset that covers multiple domains and services,and has fine-grained medical labels. The second part of the ReMeDi resourcesconsists of a set of state-of-the-art models for (medical) dialogue generation.The ReMeDi benchmark has the following methods: (1) pretrained models (i.e.,BERT-WWM, BERT-MED, GPT2, and MT5) trained, validated, and tested on the ReMeDidataset, and (2) a self-supervised contrastive learning(SCL) method to expandthe ReMeDi dataset and enhance the training of the state-of-the-art pretrainedmodels. We describe the creation of the ReMeDi dataset, the ReMeDi benchmarkingmethods, and establish experimental results using the ReMeDi benchmarkingmethods on the ReMeDi dataset for future research to compare against. With thispaper, we share the dataset, implementations of the benchmarks, and evaluationscripts.

ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues