CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
This benchmark facilitates research in Chinese biomedical AI by providing a standardized evaluation platform, though it is incremental as it extends existing English benchmarks to a new language.
The authors introduced CBLUE, the first Chinese biomedical language understanding evaluation benchmark, to address the lack of non-English benchmarks in the field, and found that state-of-the-art neural models perform significantly worse than human performance on these tasks.
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes 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, clinical diagnosis normalization, 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 experimental results show that state-of-the-art neural models perform by far worse than the human ceiling. Our benchmark is released at \url{https://tianchi.aliyun.com/dataset/dataDetail?dataId=95414&lang=en-us}.