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NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various...
08/31/2019 ∙ by Junqiu Wei, et al. ∙
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CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
In this paper, we introduce the Chinese corpus from CLUE organization, C...
03/03/2020 ∙ by Liang Xu, et al. ∙
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CLUECorpus2020: A Large-scale Chinese Corpus for Pre-trainingLanguage Model
In this paper, we introduce the Chinese corpus from CLUE organization, C...
03/03/2020 ∙ by Liang Xu, et al. ∙
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IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
Although Indonesian is known to be the fourth most frequently used langu...
09/11/2020 ∙ by Bryan Wilie, et al. ∙
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ChID: A Large-scale Chinese IDiom Dataset for Cloze Test
Cloze-style reading comprehension in Chinese is still limited due to the...
06/04/2019 ∙ by Chujie Zheng, et al. ∙
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XCMRC: Evaluating Cross-lingual Machine Reading Comprehension
We present XCMRC, the first public cross-lingual language understanding ...
08/15/2019 ∙ by Pengyuan Liu, et al. ∙
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Zero-Shot Entity Linking by Reading Entity Descriptions
We present the zero-shot entity linking task, where mentions must be lin...
06/18/2019 ∙ by Lajanugen Logeswaran, et al. ∙
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CLUE: A Chinese Language Understanding Evaluation Benchmark
04/13/2020 ∙ by Liang Xu, et al. ∙
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We introduce CLUE, a Chinese Language Understanding Evaluation benchmark. It contains eight different tasks, including single-sentence classification, sentence pair classification, and machine reading comprehension. We evaluate CLUE on a number of existing full-network pre-trained models for Chinese. We also include a small hand-crafted diagnostic test set designed to probe specific linguistic phenomena using different models, some of which are unique to Chinese. Along with CLUE, we release a large clean crawled raw text corpus that can be used for model pre-training. We release CLUE, baselines and pre-training dataset on Github.
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