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.

翻译：中文语法纠错（CGEC）在使用自回归生成模型（如序列到序列（Seq2Seq）模型和仅解码器大语言模型（LLMs））时面临严重的过度纠正挑战。先前的方法虽然旨在解决Seq2Seq模型中的过度纠正问题，但难以适用于仅解码器LLMs。本文针对过度纠正问题提出一种对齐增强的纠错器，该方法同时适用于Seq2Seq模型和仅解码器LLMs。我们首先训练一个纠错模型生成源句子的初始纠正结果，然后将源句子与初始纠正结果合并，通过对齐模型进行另一轮纠正，旨在迫使对齐模型关注潜在的过度纠正。此外，为增强模型识别细微差别的能力，我们进一步探索源句子与初始纠正结果的反向对齐。最后，我们将两个对齐模型的对齐知识迁移至纠错模型，指导其如何避免过度纠正。在三个CGEC数据集上的实验结果表明，我们的方法在缓解过度纠正和提升整体性能方面具有有效性。