Research Area:  Machine Learning
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.
Author(s) Name:  Yifan Peng, Shankai Yan, Zhiyong Lu
Journal name:  Computer Science
Publisher name:  arXiv:1906.05474
Paper Link:   https://arxiv.org/abs/1906.05474