  Whole word masking (WWM), which masks all subwords corresponding to a word at
once, makes a better English BERT model. For the Chinese language, however,
there is no subword because each token is an atomic character. The meaning of a
word in Chinese is different in that a word is a compositional unit consisting
of multiple characters. Such difference motivates us to investigate whether WWM
leads to better context understanding ability for Chinese BERT. To achieve
this, we introduce two probing tasks related to grammatical error correction
and ask pretrained models to revise or insert tokens in a masked language
modeling manner. We construct a dataset including labels for 19,075 tokens in
10,448 sentences. We train three Chinese BERT models with standard
character-level masking (CLM), WWM, and a combination of CLM and WWM,
respectively. Our major findings are as follows: First, when one character
needs to be inserted or replaced, the model trained with CLM performs the best.
Second, when more than one character needs to be handled, WWM is the key to
better performance. Finally, when being fine-tuned on sentence-level downstream
tasks, models trained with different masking strategies perform comparably.
