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
CLUE: A Chinese Language Understanding Evaluation Benchmark
The CLUE benchmark introduces a comprehensive evaluation framework for Chinese NLU with 9 tasks, using state-of-the-art pre-trained models.
- Year
- 2020
- Venue
- COLING 2020 8
- Authors
- 32
- Hosting
- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2004.05986v3ARXIV-DEFAULT
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32Liang XuDian YuZhenzhong LanZhe ZhaoKyle RichardsonJunyi LiKai SunChenjie CaoXuanwei ZhangHai HuLu LiYudong LiYechen XuCong YuYin TianQianqian DongWeitang LiuBo ShiYiming CuiJun ZengRongzhao WangWeijian XieYanting LiYina PattersonZuoyu TianYiwen ZhangHe ZhouShaoweihua LiuQipeng ZhaoCong YueXinrui ZhangZhengliang Yang