Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and various variants have been proposed to further improve the performance of the pre-trained  models. In this paper, we target on revisiting Chinese pre-trained  models to examine their effectiveness in a non-English language and release the Chinese pre-trained  model series to the community. We also propose a simple but effective model called MacBERT, which improves upon RoBERTa in several ways, especially the masking strategy  . We carried out extensive experiments on various Chinese NLP tasks , covering sentence-level to document-level, to revisit the existing pre-trained  models as well as the proposed MacBERT. Experimental results show that MacBERT could achieve state-of-the-art performances on many NLP tasks, and we also ablate details with several findings that may help future research. 
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models. In this paper, we target on revisiting Chinese pre-trained language models to examine their effectiveness in a non-English language and release the Chinese pre-trained language model series to the community. We also propose a simple but effective model called MacBERT, which improves upon RoBERTa in several ways, especially the masking strategy that adopts MLM as correction (Mac) . We carried out extensive experiments on eight Chinese NLP tasks to revisit the existing pre-trained language models as well as the proposed MacBERT. Experimental results show that MacBERT could achieve state-of-the-art performances on many NLP tasks, and we also ablate details with several findings that may help future research. Resources available: URL
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Resources available: URL