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

• URL: http://arxiv.org/abs/2110.06962v1
• Date: Wed, 13 Oct 2021 18:06:14 GMT
• Title: Open-Domain Question-Answering for COVID-19 and Other Emergent Domains
• Authors: Sharon Levy, Kevin Mo, Wenhan Xiong, William Yang Wang
• Abstract summary: We present an open-domain question-answering system for the emergent biomedical domain of COVID-19. Despite the small data size, we are able to successfully train the system to retrieve answers from a large-scale corpus of published COVID-19 scientific papers.
• Score: 61.615197623034085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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