Rethinking Masked Language Modeling for Chinese Spelling Correction
Hongqiu Wu Shaohua Zhang Yuchen Zhang Hai Zhao
In this paper, we study Chinese Spelling Correction (CSC) as a joint decision
made by two separate models: a language model and an error model. Through
empirical analysis, we find that fine-tuning BERT tends to over-fit the error
model while under-fit the language model, resulting in poor generalization to
out-of-distribution error patterns. Given that BERT is the backbone of most CSC
models, this phenomenon has a significant negative impact. To address this
issue, we are releasing a multi-domain benchmark LEMON, with higher quality and
diversity than existing benchmarks, to allow a comprehensive assessment of the
open domain generalization of CSC models. Then, we demonstrate that a very
simple strategy, randomly masking 20\% non-error tokens from the input sequence
during fine-tuning is sufficient for learning a much better language model
without sacrificing the error model. This technique can be applied to any model
architecture and achieves new state-of-the-art results on SIGHAN, ECSpell, and
LEMON.