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

Timo Möller, Anthony Reina, Raghavan Jayakumar, Malte Pietsch

Keywords: NLP, Question Answering, Dataset, COVID-19, CORD-19

TL;DR: A SQuAD style Question Answering dataset with 2.019 QA pairs annotated by medical experts (Abstract only)

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