  Under the pandemic of COVID-19, people experiencing COVID19-related symptoms
or exposed to risk factors have a pressing need to consult doctors. Due to
hospital closure, a lot of consulting services have been moved online. Because
of the shortage of medical professionals, many people cannot receive online
consultations timely. To address this problem, we aim to develop a medical
dialogue system that can provide COVID19-related consultations. We collected
two dialogue datasets -- CovidDialog -- (in English and Chinese respectively)
containing conversations between doctors and patients about COVID-19. On these
two datasets, we train several dialogue generation models based on Transformer,
GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size,
which bear high risk of overfitting, we leverage transfer learning to mitigate
data deficiency. Specifically, we take the pretrained models of Transformer,
GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune
them on our CovidDialog tasks. We perform both automatic and human evaluation
of responses generated by these models. The results show that the generated
responses are promising in being doctor-like, relevant to the conversation
history, and clinically informative. The data and code are available at
https://github.com/UCSD-AI4H/COVID-Dialogue.
