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
- Venue:
- NLP-COVID19
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2020.nlpcovid19-acl.18
- DOI:
- Cite (ACL):
- Timo Möller, Anthony Reina, Raghavan Jayakumar, and Malte Pietsch. 2020. COVID-QA: A Question Answering Dataset for COVID-19. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.
- Cite (Informal):
- COVID-QA: A Question Answering Dataset for COVID-19 (Möller et al., NLP-COVID19 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.nlpcovid19-acl.18.pdf
- Code
- deepset-ai/COVID-QA
- Data
- SQuAD