# Rapidly Bootstrapping a Question Answering Dataset for COVID-19

arXiv: https://arxiv.org/abs/2004.11339 · HTML: https://mlsanity.com/paper/2004.11339/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Hidden Backdoors in Human-Centric Language Models](https://mlsanity.com/paper/2105.00164/) (2021)
- [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021)
- [Towards Mitigating Hallucination in Large Language Models via Self-Reflection](https://mlsanity.com/paper/2310.06271/) (2023)
- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [Open-Domain Question-Answering for COVID-19 and Other Emergent Domains](https://mlsanity.com/paper/2110.06962/) (2021)
- [Synthetic Target Domain Supervision for Open Retrieval QA](https://mlsanity.com/paper/2204.09248/) (2022)
- [Establishing Strong Baselines for TripClick Health Retrieval](https://mlsanity.com/paper/2201.00365/) (2022)
- [Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering](https://mlsanity.com/paper/2206.12866/) (2022)
- [COVIDRead: A Large-scale Question Answering Dataset on COVID-19](https://mlsanity.com/paper/2110.09321/) (2021)
- [A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets](https://mlsanity.com/paper/2111.08658/) (2021)
- [Algorithmic Behaviors Across Regions: A Geolocation Audit of YouTube Search for COVID-19 Misinformation Between the United States and South Africa](https://mlsanity.com/paper/2409.10168/) (2024)
- [Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness](https://mlsanity.com/paper/2211.08386/) (2022)
- [Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentation](https://mlsanity.com/paper/2310.15015/) (2023)
- [ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese](https://mlsanity.com/paper/2504.21017/) (2025)

## Abstract (arXiv)

We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge. To our knowledge, this is the first publicly available resource of its type, and intended as a stopgap measure for guiding research until more substantial evaluation resources become available. While this dataset, comprising 124 question-article pairs as of the present version 0.1 release, does not have sufficient examples for supervised machine learning, we believe that it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19. This paper describes our methodology for constructing the dataset and presents the effectiveness of a number of baselines, including term-based techniques and various transformer-based models. The dataset is available at http://covidqa.ai/