Pre-trained Language Model for Biomedical Question Answering

Wonjin Yoon, Jinhyuk Lee, Donghyeon Kim, Minbyul Jeong, Jaewoo Kang
9/18/2019
cs.CL

Official code: dmis-lab/bioasq-biobert (126 stars, last commit 2023-03-24).

Abstract

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.

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Code Implementations(1)

dmis-lab/bioasq-biobertOfficial100%

Pre-trained Language Model for Biomedical Question Answering

12620Shell, PythonJul 26, 20193 years agoNOASSERTION

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Cite this paper

@article{yoon2019pretrained,
  title  = {Pre-trained Language Model for Biomedical Question Answering},
  author = {Wonjin Yoon and Jinhyuk Lee and Donghyeon Kim and Minbyul Jeong and Jaewoo Kang},
  year   = {2019},
  eprint = {1909.08229},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1909.08229}
}

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