Rapidly Bootstrapping a Question Answering Dataset for COVID-19

• URL: http://arxiv.org/abs/2004.11339v1
• Date: Thu, 23 Apr 2020 17:35:11 GMT
• Title: Rapidly Bootstrapping a Question Answering Dataset for COVID-19
• Authors: Raphael Tang, Rodrigo Nogueira, Edwin Zhang, Nikhil Gupta, Phuong Cam, Kyunghyun Cho, Jimmy Lin
• Abstract summary: We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19. 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.
• Score: 88.86456834766288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/

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