What Are People Asking About COVID-19? A Question Classification Dataset
This provides a domain-specific resource for developing or evaluating systems to address unanswered public questions about COVID-19, though it is incremental as it focuses on dataset creation.
The authors tackled the problem of understanding public concerns about COVID-19 by creating COVID-Q, a dataset of 1,690 questions annotated into categories and clusters, finding that many common questions lacked answers from reputable sources. They reported baseline accuracies of 58.1% for classification and 49.5% for clustering using BERT-based methods.
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.