CLUE: A Chinese Language Understanding Evaluation Benchmark

TL;DR

The paper introduces CLUE, a comprehensive Chinese language understanding benchmark with multiple tasks and models, to advance NLP research in Chinese, similar to English benchmarks like GLUE.

Contribution

It presents the first large-scale Chinese NLU benchmark with diverse tasks, datasets, and evaluation tools, enabling standardized assessment of Chinese language models.

Findings

State-of-the-art Chinese models evaluated on CLUE

Benchmark facilitates progress in Chinese NLU research

Provides supplementary datasets and tools for the community

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