  The recent success of question answering systems is largely attributed to
pre-trained language models. However, as language models are mostly pre-trained
on general domain corpora such as Wikipedia, they often have difficulty in
understanding biomedical questions. In this paper, we investigate the
performance of BioBERT, a pre-trained biomedical language model, in answering
biomedical questions including factoid, list, and yes/no type questions.
BioBERT uses almost the same structure across various question types and
achieved the best performance in the 7th BioASQ Challenge (Task 7b, Phase B).
BioBERT pre-trained on SQuAD or SQuAD 2.0 easily outperformed previous
state-of-the-art models. BioBERT obtains the best performance when it uses the
appropriate pre-/post-processing strategies for questions, passages, and
answers.
