The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.CLUEbenchmarks.com
@article{arxiv.2004.05986,
title = {CLUE: A Chinese Language Understanding Evaluation Benchmark},
author = {Liang Xu and Hai Hu and Xuanwei Zhang and Lu Li and Chenjie Cao and Yudong Li and Yechen Xu and Kai Sun and Dian Yu and Cong Yu and Yin Tian and Qianqian Dong and Weitang Liu and Bo Shi and Yiming Cui and Junyi Li and Jun Zeng and Rongzhao Wang and Weijian Xie and Yanting Li and Yina Patterson and Zuoyu Tian and Yiwen Zhang and He Zhou and Shaoweihua Liu and Zhe Zhao and Qipeng Zhao and Cong Yue and Xinrui Zhang and Zhengliang Yang and Kyle Richardson and Zhenzhong Lan},
journal= {arXiv preprint arXiv:2004.05986},
year = {2020}
}