Chinese grammatical error correction (CGEC) faces serious overcorrection challenges when employing autoregressive generative models such as sequence-to-sequence (Seq2Seq) models and decoder-only large language models (LLMs). While previous methods aim to address overcorrection in Seq2Seq models, they are difficult to adapt to decoder-only LLMs. In this paper, we propose an alignment-enhanced corrector for the overcorrection problem that applies to both Seq2Seq models and decoder-only LLMs. Our method first trains a correction model to generate an initial correction of the source sentence. Then, we combine the source sentence with the initial correction and feed it through an alignment model for another round of correction, aiming to enforce the alignment model to focus on potential overcorrection. Moreover, to enhance the model's ability to identify nuances, we further explore the reverse alignment of the source sentence and the initial correction. Finally, we transfer the alignment knowledge from two alignment models to the correction model, instructing it on how to avoid overcorrection. Experimental results on three CGEC datasets demonstrate the effectiveness of our approach in alleviating overcorrection and improving overall performance. Our code has been made publicly available.

翻译：中文语法纠错任务在采用自回归生成模型（如序列到序列模型和仅解码器大语言模型）时面临严重的过纠错挑战。现有方法虽致力于解决序列到序列模型中的过纠错问题，却难以适配仅解码器大语言模型。本文提出一种适用于序列到序列模型与仅解码器大语言模型的对齐增强纠错器，以应对过纠错问题。该方法首先训练纠错模型对源句子生成初始纠错结果；随后将源句子与初始纠错结果组合，输入对齐模型进行第二轮纠错，旨在迫使对齐模型聚焦于潜在的过纠错现象。为进一步增强模型识别细微差异的能力，我们还探索了源句子与初始纠错结果的反向对齐。最终，我们将两个对齐模型习得的对齐知识迁移至纠错模型，指导其避免过纠错行为。在三个中文语法纠错数据集上的实验结果表明，该方法能有效缓解过纠错问题并提升整体性能。相关代码已公开。