COVIDRead: A Large-scale Question Answering Dataset on COVID-19

• URL: http://arxiv.org/abs/2110.09321v1
• Date: Tue, 5 Oct 2021 07:38:06 GMT
• Title: COVIDRead: A Large-scale Question Answering Dataset on COVID-19
• Authors: Tanik Saikh, Sovan Kumar Sahoo, Asif Ekbal, Pushpak Bhattacharyya
• Abstract summary: We present a very important resource, COVIDRead, a Stanford Question Answering dataset (SQuAD) like dataset over more than 100k question-answer pairs. This is a precious resource that could serve many purposes, ranging from common people queries regarding this very uncommon disease to managing articles by editors/associate editors of a journal. We establish several end-to-end neural network based baseline models that attain the lowest F1 of 32.03% and the highest F1 of 37.19%.
• Score: 41.23094507923245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: During this pandemic situation, extracting any relevant information related to COVID-19 will be immensely beneficial to the community at large. In this paper, we present a very important resource, COVIDRead, a Stanford Question Answering Dataset (SQuAD) like dataset over more than 100k question-answer pairs. The dataset consists of Context-Answer-Question triples. Primarily the questions from the context are constructed in an automated way. After that, the system-generated questions are manually checked by hu-mans annotators. This is a precious resource that could serve many purposes, ranging from common people queries regarding this very uncommon disease to managing articles by editors/associate editors of a journal. We establish several end-to-end neural network based baseline models that attain the lowest F1 of 32.03% and the highest F1 of 37.19%. To the best of our knowledge, we are the first to provide this kind of QA dataset in such a large volume on COVID-19. This dataset creates a new avenue of carrying out research on COVID-19 by providing a benchmark dataset and a baseline model.

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