@inproceedings{wei-etal-2020-people,
    title = "What Are People Asking About {COVID-19}? A Question Classification Dataset",
    author = "Wei, Jerry  and
      Huang, Chengyu  and
      Vosoughi, Soroush  and
      Wei, Jason",
    editor = "Verspoor, Karin  and
      Cohen, Kevin Bretonnel  and
      Dredze, Mark  and
      Ferrara, Emilio  and
      May, Jonathan  and
      Munro, Robert  and
      Paris, Cecile  and
      Wallace, Byron",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.nlpcovid19-acl.8/",
    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 \url{https://github.com/JerryWei03/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."
}