  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.
