Improving Chinese Spelling Correction by Integrating Error Position Detection and Over-Correction Optimization

Chinese spelling correction is a task that involves detecting and correcting errors in Chinese text. Most character errors arise from visual or phonetic similarities, which significantly increases the difficulty of correction. In recent years, many studies have employed BERT-based language models for error correction tasks. However, these models typically adopt an encoder-decoder architecture, where the correction process relies on analyzing the context of the input sentence to predict the output. This often biases the model toward contextual information, making it susceptible to being misled by erroneous context. As a result, the model may overcorrect originally correct sentences into incorrect ones. This issue is critical for error correction tasks, as such mistakes degrade model performance and generate misleading performance metrics. This paper proposes a collaborative error correction framework that integrates a detector. The framework uses the detector to identify error locations, thereby assisting the decoder in enhancing error recognition. Additionally, a post-output optimization strategy is introduced to mitigate overcorrection errors. Comprehensive experimental results on benchmark datasets demonstrate that the proposed method achieves promising performance in Chinese text error correction tasks, significantly improving model effectiveness.