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

Sharon Levy, Kevin Mo, Wenhan Xiong, William Yang Wang


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.
Anthology ID:
2021.emnlp-demo.30
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
259–266
Language:
URL:
https://aclanthology.org/2021.emnlp-demo.30
DOI:
10.18653/v1/2021.emnlp-demo.30
Bibkey:
Cite (ACL):
Sharon Levy, Kevin Mo, Wenhan Xiong, and William Yang Wang. 2021. Open-Domain Question-Answering for COVID-19 and Other Emergent Domains. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 259–266, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains (Levy et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-demo.30.pdf
Video:
 https://aclanthology.org/2021.emnlp-demo.30.mp4
Code
 sharonlevy/open_domain_covidqa
Data
CORD-19CovidQA