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

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.

arXiv commentPublished in Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020

References in corpus (7)

• Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set
• Measuring Emotions in the COVID-19 Real World Worry Dataset
• A First Instagram Dataset on COVID-19
• NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset
• CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset
• Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19
• FAQ Retrieval using Query-Question Similarity and BERT-Based Query-Answer Relevance

Cited by in corpus (3)

• Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19
• Answering Questions on COVID-19 in Real-Time
• orgFAQ: A New Dataset and Analysis on Organizational FAQs and User Questions