COVID-QA: A Question Answering Dataset for COVID-19

Timo Möller, Anthony Reina, Raghavan Jayakumar, Malte Pietsch


Abstract
We present COVID-QA, a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. To evaluate the dataset we compared a RoBERTa base model fine-tuned on SQuAD with the same model trained on SQuAD and our COVID-QA dataset. We found that the additional training on this domain-specific data leads to significant gains in performance. Both the trained model and the annotated dataset have been open-sourced at: https://github.com/deepset-ai/COVID-QA
Anthology ID:
2020.nlpcovid19-acl.18
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.18
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.nlpcovid19-acl.18.pdf
Code
 deepset-ai/COVID-QA
Data
SQuAD