Evolving Chinese Spelling Correction with Corrector-Verifier Collaboration

摘要

Recent methods address Chinese Spelling Correction (CSC) with either BERT-based models or large language models (LLMs) independently. However, both of them face challenges. BERT-based models are efficient for this task but struggle with limited generalizability to error patterns, thus failing in open-domain CSC. LLMs are advantageous in their extensive knowledge but fall into low efficiency in character-level editing. To address this dilemma, we propose Automatic Corrector Iteration (ACI), a novel model collaboration pipeline to iteratively optimize a BERT-based corrector. This pipeline is free of human annotation, by leveraging an LLM verifier to provide useful signals for the corrector. Experimental results demonstrate that our pipeline consistently improves the model performance across iterations and significantly outperforms existing data augmentation methods, achieving comparable performance with human annotation.