BERT-based Automatic Error Correction System for Chinese Learners

Abstract

Current automatic error correction methods for Chinese learners often focus on superficial word- or sentence-level processing and are affected by inconsistent annotation standards, resulting in limited generalization to real learner texts and causing misalignment or overcorrection. To improve grammatical correctness, semantic fidelity, and instructional relevance, this paper constructs a multi-granularity lexical representation method. Using characters as the basic unit, the method integrates three types of information: characters, words, and pinyin. These representations are learned through projection processing, concatenated into a unified embedding, and fed into a pre-trained Chinese BERT encoder. A dual-task head consisting of sequence labeling and lightweight generation is deployed on the shared BERT encoder for collaborative optimization. Key innovations include an alignment consistency loss to ensure character-level consistency, and the combination of word-segmentation augmentation and multi-reference soft-label training to reduce conflicts caused by segmentation differences. Experimental results show that the method achieves granular alignment consistency above 0.800, corrected-original sentence similarity of 0.888, and an overcorrection rate as low as 0.041. These results demonstrate improved robustness and explainability for automatic Chinese learner error correction.