  Biomedical question answering (QA) is a challenging task due to the scarcity
of data and the requirement of domain expertise. Pre-trained language models
have been used to address these issues. Recently, learning relationships
between sentence pairs has been proved to improve performance in general QA. In
this paper, we focus on applying BioBERT to transfer the knowledge of natural
language inference (NLI) to biomedical QA. We observe that BioBERT trained on
the NLI dataset obtains better performance on Yes/No (+5.59%), Factoid
(+0.53%), List type (+13.58%) questions compared to performance obtained in a
previous challenge (BioASQ 7B Phase B). We present a sequential transfer
learning method that significantly performed well in the 8th BioASQ Challenge
(Phase B). In sequential transfer learning, the order in which tasks are
fine-tuned is important. We measure an unanswerable rate of the extractive QA
setting when the formats of factoid and list type questions are converted to
the format of the Stanford Question Answering Dataset (SQuAD).
