Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

Yifan Peng, Shankai Yan, Zhiyong Lu


Abstract
Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate research in the development of pre-training language representations in the biomedicine domain. The benchmark consists of five tasks with ten datasets that cover both biomedical and clinical texts with different dataset sizes and difficulties. We also evaluate several baselines based on BERT and ELMo and find that the BERT model pre-trained on PubMed abstracts and MIMIC-III clinical notes achieves the best results. We make the datasets, pre-trained models, and codes publicly available at https://github.com/ ncbi-nlp/BLUE_Benchmark.
Anthology ID:
W19-5006
Volume:
Proceedings of the 18th BioNLP Workshop and Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–65
Language:
URL:
https://aclanthology.org/W19-5006
DOI:
10.18653/v1/W19-5006
Bibkey:
Cite (ACL):
Yifan Peng, Shankai Yan, and Zhiyong Lu. 2019. Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 58–65, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets (Peng et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5006.pdf
Code
 ncbi-nlp/NCBI_BERT +  additional community code
Data
BLUEBC5CDRBIOSSESChemProtDDIGLUEHOCMIMIC-IIIMedNLI