  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.
