  Chinese Spelling Correction (CSC) commonly lacks large-scale high-quality
corpora, due to the labor-intensive labeling of spelling errors in real-life
human writing or typing scenarios. Two data augmentation methods are widely
adopted: (1) \textit{Random Replacement} with the guidance of confusion sets
and (2) \textit{OCR/ASR-based Generation} that simulates character misusing.
However, both methods inevitably introduce noisy data (e.g., false spelling
errors), potentially leading to over-correction. By carefully analyzing the two
types of corpora, we find that though the latter achieves more robust
generalization performance, the former yields better-calibrated CSC models. We
then provide a theoretical analysis of this empirical observation, based on
which a corpus refining strategy is proposed. Specifically, OCR/ASR-based data
samples are fed into a well-calibrated CSC model trained on random
replacement-based corpora and then filtered based on prediction confidence. By
learning a simple BERT-based model on the refined OCR/ASR-based corpus, we set
up impressive state-of-the-art performance on three widely-used benchmarks,
while significantly alleviating over-correction (e.g., lowering false positive
predictions).
