TY - GEN
T1 - CBLUE
T2 - 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022
AU - Zhang, Ningyu
AU - Chen, Mosha
AU - Bi, Zhen
AU - Liang, Xiaozhuan
AU - Li, Lei
AU - Shang, Xin
AU - Yin, Kangping
AU - Tan, Chuanqi
AU - Xu, Jian
AU - Huang, Fei
AU - Si, Luo
AU - Ni, Yuan
AU - Xie, Guotong
AU - Sui, Zhifang
AU - Chang, Baobao
AU - Zong, Hui
AU - Yuan, Zheng
AU - Li, Linfeng
AU - Yan, Jun
AU - Zan, Hongying
AU - Zhang, Kunli
AU - Tang, Buzhou
AU - Chen, Qingcai
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - With the development of biomedical language understanding benchmarks, Artificial Intelligence 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, 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 far worse than the human ceiling. Our benchmark is released at https://tianchi.aliyun.com/dataset/dataDetail?dataId= 95414&lang=en-us.
AB - With the development of biomedical language understanding benchmarks, Artificial Intelligence 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, 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 far worse than the human ceiling. Our benchmark is released at https://tianchi.aliyun.com/dataset/dataDetail?dataId= 95414&lang=en-us.
UR - https://www.scopus.com/pages/publications/85136117528
U2 - 10.18653/v1/2022.acl-long.544
DO - 10.18653/v1/2022.acl-long.544
M3 - 会议稿件
AN - SCOPUS:85136117528
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 7888
EP - 7915
BT - ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
A2 - Muresan, Smaranda
A2 - Nakov, Preslav
A2 - Villavicencio, Aline
PB - Association for Computational Linguistics (ACL)
Y2 - 22 May 2022 through 27 May 2022
ER -