text: In recent years Neural sequence-to-sequence (seq2seq) models have come to be considered proven approaches to grammatical error correction (GEC). But most seq2seq models for GEC have flaws, which limits the training scale to only limited error-corrected sentence pairs and to sentences with limited grammatical errors through single-round seq2seq inference — especially if particular errors in a sentence make the context unclear, which can confuse the model’s subsequent error-corrections.