In response to the Kaggle's COVID-19 Open Research Dataset (CORD-19) challenge, we have proposed three transformer-based question-answering systems using BERT, ALBERT, and T5 models. Since the CORD-19 dataset is unlabeled, we have evaluated the question-answering models' performance on two labeled questions answers datasets \textemdash CovidQA and CovidGQA. The BERT-based QA system achieved the highest F1 score (26.32), while the ALBERT-based QA system achieved the highest Exact Match (13.04). However, numerous challenges are associated with developing high-performance question-answering systems for the ongoing COVID-19 pandemic and future pandemics. At the end of this paper, we discuss these challenges and suggest potential solutions to address them.
@article{arxiv.2101.11432,
title = {Transformer-Based Models for Question Answering on COVID19},
author = {Hillary Ngai and Yoona Park and John Chen and Mahboobeh Parsapoor},
journal= {arXiv preprint arXiv:2101.11432},
year = {2021}
}