Transformer-Based Models for Question Answering on COVID19

• URL: http://arxiv.org/abs/2101.11432v1
• Date: Sat, 16 Jan 2021 23:06:30 GMT
• Title: Transformer-Based Models for Question Answering on COVID19
• Authors: Hillary Ngai, Yoona Park, John Chen and Mahboobeh Parsapoor (Mah Parsa)
• Abstract summary: We propose three transformer-based question-answering systems using BERT, ALBERT, and T5 models. 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)
• Score: 4.631723879329972
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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