提出新模型NamBert，用音形特征提升中文错别字纠正效果
Unveiling the Impact of Multimodal Features on Chinese Spelling Correction: From Analysis to Design
- 融合语音与字形信息，设计多模态分析框架
- 在标准数据集上超越现有最优方法，减少过度修正问题
- 适合需要高准确率纠错的中文NLP应用
中文错别字纠正（CSC）旨在检测并修正句子中的拼写错误。当前研究主要采用传统多模态预训练模型和大语言模型（LLMs）。然而，LLMs在CSC任务中存在过度修正等局限性，表现不佳。尽管已有研究探索了语音与字形信息在多模态CSC模型中的应用，但如何有效利用这些特征仍具挑战。为此，我们提出多模态字符使用分析实验（MACU），揭示多模态纠错的优化方向。基于实证发现，我们构建了名为NamBert的新多模态模型。在多个基准数据集上的实验表明，NamBert性能优于当前最优方法。我们还系统比较了NamBert与LLMs在CSC中的优劣。代码与模型已开源。
原文摘要 · Abstract (English)
The Chinese Spelling Correction (CSC) task focuses on detecting and correcting spelling errors in sentences. Current research primarily explores two approaches: traditional multimodal pre-trained models and large language models (LLMs). However, LLMs face limitations in CSC, particularly over-correction, making them suboptimal for this task. While existing studies have investigated the use of phonetic and graphemic information in multimodal CSC models, effectively leveraging these features to enhance correction performance remains a challenge. To address this, we propose the Multimodal Analysis for Character Usage (\textbf{MACU}) experiment, identifying potential improvements for multimodal correctison. Based on empirical findings, we introduce \textbf{NamBert}, a novel multimodal model for Chinese spelling correction. Experiments on benchmark datasets demonstrate NamBert's superiority over SOTA methods. We also conduct a comprehensive comparison between NamBert and LLMs, systematically evaluating their strengths and limitations in CSC. Our code and model are available at https://github.com/iioSnail/NamBert.
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