CLUE: A Chinese Language Understanding Evaluation Benchmark
This addresses the problem of limited NLU benchmarks for Chinese, enabling broader research and applications in Chinese NLP, though it is incremental as it adapts existing benchmark concepts to a new language.
The authors tackled the lack of non-English natural language understanding benchmarks by introducing CLUE, the first large-scale Chinese Language Understanding Evaluation benchmark, which includes 9 tasks and reports scores using 9 state-of-the-art pre-trained Chinese models.
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