  The recent outbreak of the novel coronavirus is wreaking havoc on the world
and researchers are struggling to effectively combat it. One reason why the
fight is difficult is due to the lack of information and knowledge. In this
work, we outline our effort to contribute to shrinking this knowledge vacuum by
creating covidAsk, a question answering (QA) system that combines biomedical
text mining and QA techniques to provide answers to questions in real-time. Our
system also leverages information retrieval (IR) approaches to provide
entity-level answers that are complementary to QA models. Evaluation of
covidAsk is carried out by using a manually created dataset called COVID-19
Questions which is based on information from various sources, including the CDC
and the WHO. We hope our system will be able to aid researchers in their search
for knowledge and information not only for COVID-19, but for future pandemics
as well.
