CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

Mosha Chen, Bi Zhen, Xiaozhuan Liang, Lei Li, Ningyu Zhang, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan et al. (3 additional authors not shown)

Keywords: Chinese Biomedical Language Understanding, Benchmark, Dataset

TL;DR: A Chinese biomedical language understanding evaluation benchmark

Abstract: Artificial intelligence (AI), along with the recent progress in biomedical language understanding, is gradually offering great promise for medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most such benchmarks are limited to English, which has made it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and results show that state-of-the-art neural models perform by far worse than the human ceiling. Our benchmark is released at https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us.

URL: https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2106.08087/code)