关键词: 自然语言处理, 特征增强, 中文拼写纠错, 语义融合, 文本纠错, 预训练语言模型

Abstract: As current leading Chinese Spelling Correction (CSC) model, ReLM (Rephrasing Language Model) was found to have insufficient feature representation in complex semantic scenarios. To address this issue, FeReLM(Feature-enhanced Rephrasing Language Model) was proposed. The model was designed to leverage Depthwise Separable Convolution (DSC) to integrate deep semantic features generated by feature extraction model BGE(BAAI General Embedding) with global features produced by ReLM, effectively enhancing model’s ability to parse complex contexts and improve precision in recognizing and correcting spelling errors. Initially, FeReLM was trained on Wang271K dataset, enabling it to continuously learn deep semantics and complex expressions within sentences. Subsequently, the learned weights were transferred to new datasets for fine-tuning. Experimental results show that FeReLM outperforms ReLM, MCRSpell (Metric learning of Correct Representation for Chinese Spelling Correction), RSpell(Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check) and so on in key metrics such as precision, recall, and F1 score, with improvements ranging from 0.6 to 28.7 percentage points. The effectiveness of proposed method is confirmed through ablation experiments.

Key words: Natural Language Processing &#40, NLP&#41, feature enhancement, Chinese Spelling Correction &#40, CSC&#41