Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
This addresses the need for accurate information in emergent domains like COVID-19 to combat misinformation, though it is incremental as it adapts existing techniques.
The authors tackled the problem of providing credible answers to COVID-19 questions by developing an open-domain question-answering system that retrieves answers from a corpus of scientific papers, achieving successful training despite limited data.
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.