  This paper studies Chinese Spelling Correction (CSC), which aims to detect
and correct the potential spelling errors in a given sentence. Current
state-of-the-art methods regard CSC as a sequence tagging task and fine-tune
BERT-based models on sentence pairs. However, we note a critical flaw in the
process of tagging one character to another, that the correction is excessively
conditioned on the error. This is opposite from human mindset, where
individuals rephrase the complete sentence based on its semantics, rather than
solely on the error patterns memorized before. Such a counter-intuitive
learning process results in the bottleneck of generalizability and
transferability of machine spelling correction. To address this, we propose
Rephrasing Language Model (ReLM), where the model is trained to rephrase the
entire sentence by infilling additional slots, instead of
character-to-character tagging. This novel training paradigm achieves the new
state-of-the-art results across fine-tuned and zero-shot CSC benchmarks,
outperforming previous counterparts by a large margin. Our method also learns
transferable language representation when CSC is jointly trained with other
tasks.
