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 QAby 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).
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).
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problem
task
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Growing interests of using pre-trained language models with transfer learning address the issue to some extent
Pre-trained language models have been used to address these issues
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linguistic knowledge of entailment in sentence pairs enhances the
relationships between sentence pairs has been proved to improve
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domain QAby leveraging such transferability between the two tasks
QA
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facilitating the transferability by unifying the experimental setup from
applying BioBERT to transfer the knowledge of
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transferring from entailment data shows effective
BioBERT trained on the NLI dataset obtains better
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previous challenge reports
performance obtained in a previous challenge
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also observe that our method generally performs
present a sequential transfer learning method that significantly performed
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of how
in which
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In factoid- and list-type questions, we thoroughly analyze an intrinsic limitation
We measure an unanswerable rate
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the formats of factoid and list type
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same