Open-Domain Question-Answering for COVID-19 and Other Emergent Domains

Abstract

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 questionanswering techniques, such as document diversity and multiple answer spans. Our opendomain 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.

DuReadervis: A : A Chinese Dataset for Open-domain Document Visual Question Answering

“…• We propose a simple baseline method as the open-domain DocVQA baseline, and the gap between the baseline and human performance shows huge room for improvement. (Kwiatkowski et al, 2019); SQuADopen (Chen et al, 2017b); SearchQA (Dunn et al, 2017); MS-MARCO (Nguyen et al, 2016)), enterprise search (Castelli et al, 2020) to biomedical QA about COVID (Levy et al, 2021).…”