Transferability of Natural Language Inference to Biomedical Question Answering

Biomedical question answering (QA) is a challenging problem due to the scarcity of data and the requirement of domain expertise. Growing interests of using pre-trained language models with transfer learning address the issue to some extent. Recently, learning linguistic knowledge of entailment in sentence pairs enhances the performance in general domain QA by leveraging such transferability between the two tasks. In this paper, we focus on facilitating the transferability by unifying the experimental setup from natural language inference (NLI) to biomedical QA. We observe that transferring from entailment data shows effective performance on Yes/No (+5.59%), Factoid (+0.53%), List (+13.58%) type questions compared to previous challenge reports (BioASQ 7B Phase B). We also observe that our method generally performs well in the 8th BioASQ Challenge (Phase B). For sequential transfer learning, the order of how tasks are fine-tuned is important. In factoid- and list-type questions, we thoroughly analyze an intrinsic limitation of the extractive QA setting when these questions are converted to the same format of the Stanford Question Answering Dataset (SQuAD).