"Is Whole Word Masking Always Better for Chinese BERT?": Probing on Chinese Grammatical Error Correction

TL;DR

This study investigates whether whole word masking improves Chinese BERT's understanding, especially for grammatical error correction, revealing that character-level masking is better for single-character edits, while WWM benefits multi-character handling.

Contribution

The paper introduces probing tasks and a dataset for Chinese grammatical error correction to compare masking strategies in BERT models.

Findings

CLM performs better for single-character edits.

WWM improves performance on multi-character edits.

Different masking strategies yield similar results on sentence-level tasks.

Abstract

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.