CLUE: A Chinese Language Understanding Evaluation Benchmark
Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, 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 and Zhenzhong Lan CLUE team ∗ Corresponding author. E-mail:
Abstract
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
中文速览
中文自然语言理解长期缺乏一个像英文 GLUE 那样覆盖面广、标准统一的评测基准，导致研究者难以横向比较模型、推动技术进步。为此，作者构建了首个大规模中文语言理解评测基准 CLUE（Chinese Language Understanding Evaluation），涵盖文本分类、语义相似度、自然语言推理和机器阅读理解等共9项任务，其中两项为作者自行创建，所有任务均基于原生中文文本。他们还配套发布了超过214GB、约760亿词的中文预训练语料库、一套由语言学家手工标注的诊断数据集，以及在线排行榜和易用工具包，并用9个当前主流中文预训练模型（如BERT、ALBERT、RoBERTa等）在所有任务上进行了系统测评。结果显示各任务难度不一，且机器与人类表现仍存在明显差距，这一基准的推出为中文NLP研究提供了统一的评测平台，有望显著加速中文语言理解技术的发展。
原文 arXiv:2004.05986;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2004.05986v3