Pre-trained Language Model for Biomedical Question Answering

Wonjin Yoon; Jinhyuk Lee; Donghyeon Kim; Minbyul Jeong; Jaewoo Kang

doi:10.1007/978-3-030-43887-6_64

Pre-trained Language Model for Biomedical Question Answering

Wonjin Yoon, Jinhyuk Lee, Donghyeon Kim, Minbyul Jeong, Jaewoo Kang

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

40 Citations (Scopus)

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.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings
Editors	Peggy Cellier, Kurt Driessens
Publisher	Springer
Pages	727-740
Number of pages	14
ISBN (Print)	9783030438869
DOIs	https://doi.org/10.1007/978-3-030-43887-6_64
Publication status	Published - 2020
Event	19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany Duration: 2019 Sept 16 → 2019 Sept 20

Publication series

Name	Communications in Computer and Information Science
Volume	1168 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Country/Territory	Germany
City	Wurzburg
Period	19/9/16 → 19/9/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Biomedical question answering
Pre-trained language model
Transfer learning

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-030-43887-6_64

Cite this

Yoon, W., Lee, J., Kim, D., Jeong, M., & Kang, J. (2020). Pre-trained Language Model for Biomedical Question Answering. In P. Cellier, & K. Driessens (Eds.), Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings (pp. 727-740). (Communications in Computer and Information Science; Vol. 1168 CCIS). Springer. https://doi.org/10.1007/978-3-030-43887-6_64

Pre-trained Language Model for Biomedical Question Answering. / Yoon, Wonjin; Lee, Jinhyuk; Kim, Donghyeon et al.
Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings. ed. / Peggy Cellier; Kurt Driessens. Springer, 2020. p. 727-740 (Communications in Computer and Information Science; Vol. 1168 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yoon, W, Lee, J, Kim, D, Jeong, M & Kang, J 2020, Pre-trained Language Model for Biomedical Question Answering. in P Cellier & K Driessens (eds), Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings. Communications in Computer and Information Science, vol. 1168 CCIS, Springer, pp. 727-740, 19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, Wurzburg, Germany, 19/9/16. https://doi.org/10.1007/978-3-030-43887-6_64

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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.",

keywords = "Biomedical question answering, Pre-trained language model, Transfer learning",

author = "Wonjin Yoon and Jinhyuk Lee and Donghyeon Kim and Minbyul Jeong and Jaewoo Kang",

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Pre-trained Language Model for Biomedical Question Answering

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this