Transformer-Based Models for Question Answering on COVID19
This work addresses the need for automated information retrieval from COVID-19 research, but it is incremental as it applies existing models to new data without major methodological innovations.
The paper tackled the problem of question answering on COVID-19 literature by proposing transformer-based systems using BERT, ALBERT, and T5 models, with the BERT-based system achieving the highest F1 score of 26.32 and the ALBERT-based system achieving the highest Exact Match of 13.04 on labeled datasets.
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.