Optimisation of intelligent English grammar error correction based on multi-strategy Pinyin detection and hierarchical enhancement

This study proposes an intelligent grammar correction method integrating multi-strategy Pinyin detection and hierarchical data augmentation to address common errors in Chinese English learners' writing.A dual-strategy Pinyin detection algorithm combines syllable tree matching and linguistic rules to accurately identify and preserve Pinyin segments.A hierarchical data augmentation approach employs rule-based and model-based back-translation to build diverse parallel corpora targeting typical learner errors.Based on the Transformer architecture, the grammar correction model treats error correction as a sequence-to-sequence task.Results show the Pinyin detector achieves 99.95% accuracy, processing 5,386 words/second with 13.02 MB memory usage.The correction model attains a 40.58 F0.5 score and 49.56% accuracy on CoNLL-2014.On CLEC subsets, it achieves 87.5%, 90.2%, and 85.9% accuracy for article, subject-verb agreement, and verb tense errors, respectively.Pinyin false corrections dropped from 65% to 1.8%, demonstrating significant improvement in handling Chinese learners' English writing.