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CLUE: A Chinese Language Understanding Evaluation Benchmark

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arxiv 2004.05986 v3 pith:BZGOBBFG submitted 2020-04-13 cs.CL cs.LG

Liang Xu , Hai Hu , Xuanwei Zhang , Lu Li , Chenjie Cao , Yudong Li , Yechen Xu , Kai Sun
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Dian Yu Cong Yu Yin Tian Qianqian Dong Weitang Liu Bo Shi Yiming Cui Junyi Li Jun Zeng Rongzhao Wang Weijian Xie Yanting Li Yina Patterson Zuoyu Tian Yiwen Zhang He Zhou Shaoweihua Liu Zhe Zhao Qipeng Zhao Cong Yue Xinrui Zhang Zhengliang Yang Kyle Richardson Zhenzhong Lan
This is my paper · ORCID
classification cs.CLcs.LG
keywords chineselanguagetasksbenchmarkbenchmarksclueenglishunderstanding
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 3 Pith papers

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