  Medical dialogue systems (MDSs) aim to assist doctors and patients with a
range of professional medical services, i.e., diagnosis, treatment and
consultation. The development of MDSs is hindered because of a lack of
resources. In particular. (1) there is no dataset with large-scale medical
dialogues that covers multiple medical services and contains fine-grained
medical labels (i.e., intents, actions, slots, values), and (2) there is no set
of established benchmarks for MDSs for multi-domain, multi-service medical
dialogues. In this paper, we present ReMeDi, a set of resource for medical
dialogues. ReMeDi consists of two parts, the ReMeDi dataset and the ReMeDi
benchmarks. The ReMeDi dataset contains 96,965 conversations between doctors
and patients, including 1,557 conversations with fine-gained labels. It covers
843 types of diseases, 5,228 medical entities, and 3 specialties of medical
services across 40 domains. To the best of our knowledge, the ReMeDi dataset is
the only medical dialogue dataset that covers multiple domains and services,
and has fine-grained medical labels. The second part of the ReMeDi resources
consists 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 ReMeDi
dataset, and (2) a self-supervised contrastive learning(SCL) method to expand
the ReMeDi dataset and enhance the training of the state-of-the-art pretrained
models. We describe the creation of the ReMeDi dataset, the ReMeDi benchmarking
methods, and establish experimental results using the ReMeDi benchmarking
methods on the ReMeDi dataset for future research to compare against. With this
paper, we share the dataset, implementations of the benchmarks, and evaluation
scripts.
