Abstract
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 overftting, 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 datasets. Experiments demonstrate that
these approaches are promising in generating meaningful medical
dialogues about COVID-19. But more advanced approaches are needed to
build a fully useful dialogue system that can offer accurate
COVID-related consultations. The data and code are available at
https://github.com/UCSD-AI4H/COVID-Dialogue