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
- Editors:
- Heike Adel, Shuming Shi
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.emnlp-demo.30.pdf
- Code
- sharonlevy/open_domain_covidqa
- Data
- CORD-19, CovidQA