Fine-Tuning BERT for Question and Answering Using PubMed Abstract Dataset
Saeyeon Cheon, Insung Ahn · 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · 2022
The coronavirus, which first originated in China in 2019, spread worldwide and eventually reached a pandemic situation. In the interest of many people, misinformation about the coronavirus has been pouring out on the Internet. We developed a Q&A processing technique by building a dataset based on the PubMed paper abstract for people to easily get the right information. We fine-tuned BioBERT among the BERT models that reached SOTA performance in the biomedical Q&A task. It answered questions about coronavirus with high accuracy. In the future, we will develop our technology that can handle Q&A not only in English but also in multiple languages. This work will contribute to helping people who speak different languages easily obtain correct information amidst confusing data.