We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50% time cost for inference.

本文提出了一种新颖的面向语言无关的语法错误修正方法，将任务分为两个子任务：错误跨度检测（Erroneous Span Detection, ESD) 和错误跨度修正（Erroneous Span Correction, ESC），ESD 使用高效的序列标注模型识别具有语法错误的文本跨度，而 ESC 则使用序列到序列模型，接受标注错误跨度的句子作为输入，并仅输出这些跨度的纠正文本。实验结果表明，我们的方法在英文和汉语 GEC 基准测试中的表现与传统的 seq2seq 方法相当，但推断时间成本不到 50%。

通过错误跨度的检测和更正提升语法错误修正的效率