  The Covid-19 pandemic has caused a spur in the medical research literature.
With new research advances in understanding the virus, there is a need for
robust text mining tools which can process, extract and present answers from
the literature in a concise and consumable way. With a DialoGPT based
multi-turn conversation generation module, and BM-25 \& neural embeddings based
ensemble information retrieval module, in this paper we present a
conversational system, which can retrieve and answer coronavirus-related
queries from the rich medical literature, and present it in a conversational
setting with the user. We further perform experiments to compare neural
embedding-based document retrieval and the traditional BM25 retrieval algorithm
and report the results.
