  The development of conversational agents to interact with patients and
deliver clinical advice has attracted the interest of many researchers,
particularly in light of the COVID-19 pandemic. The training of an end-to-end
neural based dialog system, on the other hand, is hampered by a lack of
multi-turn medical dialog corpus. We make the very first attempt to release a
high-quality multi-turn Medical Dialog dataset relating to Covid-19 disease
named CDialog, with over 1K conversations collected from the online medical
counselling websites. We annotate each utterance of the conversation with seven
different categories of medical entities, including diseases, symptoms, medical
tests, medical history, remedies, medications and other aspects as additional
labels. Finally, we propose a novel neural medical dialog system based on the
CDialog dataset to advance future research on developing automated medical
dialog systems. We use pre-trained language models for dialogue generation,
incorporating annotated medical entities, to generate a virtual doctor's
response that addresses the patient's query. Experimental results show that the
proposed dialog models perform comparably better when supplemented with entity
information and hence can improve the response quality.
