mlsanityData to 7 Oct 2026

Rapidly Bootstrapping a Question Answering Dataset for COVID-19

arXiv 2004.11339 ↗First posted 23 Apr 2020cs.CL cs.AI cs.IRcited by 14

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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/

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2020.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 2; 2024: 1; 2025: 1.

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