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

Jerry Wei, Chengyu Huang, Soroush Vosoughi, Jason Wei


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/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.
Anthology ID:
2020.nlpcovid19-acl.8
Volume:
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | NLP-COVID19
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/2020.nlpcovid19-acl.8
DOI:
Bibkey:
Cite (ACL):
Jerry Wei, Chengyu Huang, Soroush Vosoughi, and Jason Wei. 2020. What Are People Asking About COVID-19? A Question Classification Dataset. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.
Cite (Informal):
What Are People Asking About COVID-19? A Question Classification Dataset (Wei et al., NLP-COVID19 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.nlpcovid19-acl.8.pdf
Code
 JerryWei03/COVID-Q
Data
COVID-Q