  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.
