Transferability of Natural Language Inference to Biomedical Question Answering

Minbyul Jeong, Mujeen Sung, Gangwoo Kim, Donghyeon Kim, Wonjin Yoon, Jaehyo Yoo, Jaewoo Kang

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


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).

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 2020
Event11th Conference and Labs of the Evaluation Forum, CLEF 2020 - Thessaloniki, Greece
Duration: 2020 Sept 222020 Sept 25

Bibliographical note

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  • Biomedical question answering
  • Domain adaptation
  • Natural language inference
  • Transfer learning

ASJC Scopus subject areas

  • General Computer Science


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