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

arXiv: https://arxiv.org/abs/2110.06962 · HTML: https://mlsanity.com/paper/2110.06962/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval](https://mlsanity.com/paper/2009.12756/) (2020)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [Rapidly Bootstrapping a Question Answering Dataset for COVID-19](https://mlsanity.com/paper/2004.11339/) (2020)
- [Progressively Pretrained Dense Corpus Index for Open-Domain Question Answering](https://mlsanity.com/paper/2005.00038/) (2020)
- [End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training](https://mlsanity.com/paper/2012.01414/) (2020)
- [AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature](https://mlsanity.com/paper/2007.09186/) (2020)

## Built on by (most cited first, 7 of 7)

- [Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations](https://mlsanity.com/paper/2408.15232/) (2024)
- [OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking](https://mlsanity.com/paper/2501.09751/) (2025)
- [Mitigating Covertly Unsafe Text within Natural Language Systems](https://mlsanity.com/paper/2210.09306/) (2022)
- [SpaceQA: Answering Questions about the Design of Space Missions and Space Craft Concepts](https://mlsanity.com/paper/2210.03422/) (2022)
- [Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains](https://mlsanity.com/paper/2201.11153/) (2022)
- [Descriptive Knowledge Graph in Biomedical Domain](https://mlsanity.com/paper/2310.11681/) (2023)
- [Using Weak Supervision and Data Augmentation in Question Answering](https://mlsanity.com/paper/2309.16175/) (2023)

## Abstract (arXiv)

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.