  Since late 2019, COVID-19 has quickly emerged as the newest biomedical
domain, resulting in a surge of new information. As with other emergent
domains, the discussion surrounding the topic has been rapidly changing,
leading to the spread of misinformation. This has created the need for a public
space for users to ask questions and receive credible, scientific answers. To
fulfill this need, we turn to the task of open-domain question-answering, which
we can use to efficiently find answers to free-text questions from a large set
of documents. In this work, we present such a system for the emergent domain of
COVID-19. Despite the small data size available, we are able to successfully
train the system to retrieve answers from a large-scale corpus of published
COVID-19 scientific papers. Furthermore, we incorporate effective re-ranking
and question-answering techniques, such as document diversity and multiple
answer spans. Our open-domain question-answering system can further act as a
model for the quick development of similar systems that can be adapted and
modified for other developing emergent domains.
