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CLUE: A Chinese Language Understanding Evaluation Benchmark

TLDR
The first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark is introduced, 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.
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

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Pre-Training with Whole Word Masking for Chinese BERT

TL;DR: The whole word masking (wwm) strategy for Chinese BERT is introduced, along with a series of Chinese pre-trained language models, and a simple but effective model called MacBERT is proposed, which improves upon RoBERTa in several ways.
Journal ArticleDOI

A Survey of Large Language Models

TL;DR: Recently, a large language model (LLM) as mentioned in this paper has been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks.
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GLM-130B: An Open Bilingual Pre-trained Model

TL;DR: An attempt to open-source a 100B-scale model at least as good as GPT-3 and unveil how models of such a scale can be successfully pre-trained, including its design choices, training strategies for both efficiency and stability, and engineering efforts is introduced.
Proceedings ArticleDOI

Improving Sign Language Translation with Monolingual Data by Sign Back-Translation

TL;DR: This article proposed a sign back-translation (SignBT) approach, which incorporates massive spoken language texts into SLT training to tackle the parallel data bottleneck, and obtained a substantial improvement over previous state-of-the-art SLT methods.
Proceedings ArticleDOI

OCNLI: Original Chinese Natural Language Inference

TL;DR: The Original Chinese Natural Language Inference dataset (OCNLI) as mentioned in this paper is the first large-scale natural language inference dataset for Chinese, consisting of 56,000 annotated sentence pairs.
References
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Attention is All you Need

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Distributed Representations of Words and Phrases and their Compositionality

TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
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RoBERTa: A Robustly Optimized BERT Pretraining Approach

TL;DR: It is found that BERT was significantly undertrained, and can match or exceed the performance of every model published after it, and the best model achieves state-of-the-art results on GLUE, RACE and SQuAD.
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