What Are People Asking About COVID-19? A Question Classification Dataset

May 26, 2020 · Entered Twilight · 🏛 NLPCOVID19

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Repo contents: Figure.png, README.md, code, data, dataset_categories, dataset_classes, final_master_dataset.csv

Authors Jerry Wei, Chengyu Huang, Soroush Vosoughi, Jason Wei arXiv ID 2005.12522 Category cs.CL: Computation & Language Citations 34 Venue NLPCOVID19 Repository https://github.com/JerryWeiAI/COVID-Q ⭐ 24 Last Checked 1 month ago

Abstract

We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters. The most common questions in our dataset asked about transmission, prevention, and societal effects of COVID, and we found that many questions that appeared in multiple sources were not answered by any FAQ websites of reputable organizations such as the CDC and FDA. We post our dataset publicly at https://github.com/JerryWeiAI/COVID-Q. For classifying questions into 15 categories, a BERT baseline scored 58.1% accuracy when trained on 20 examples per category, and for a question clustering task, a BERT + triplet loss baseline achieved 49.5% accuracy. We hope COVID-Q can help either for direct use in developing applied systems or as a domain-specific resource for model evaluation.