Synthetic data construction of Grammatical Error Correction (GEC) for
non-English languages relies heavily on human-designed and language-specific
rules, which produce limited error-corrected patterns. In this paper, we
propose a generic and language-independent strategy for multilingual GEC, which
can train a GEC system effectively for a new non-English language with only two
easy-to-access resources: 1) a pretrained cross-lingual language model (PXLM)
and 2) parallel translation data between English and the language. Our approach
creates diverse parallel GEC data without any language-specific operations by
taking the non-autoregressive translation generated by PXLM and the gold
translation as error-corrected sentence pairs. Then, we reuse PXLM to
initialize the GEC model and pretrain it with the synthetic data generated by
itself, which yields further improvement. We evaluate our approach on three
public benchmarks of GEC in different languages. It achieves the
state-of-the-art results on the NLPCC 2018 Task 2 dataset (Chinese) and obtains
competitive performance on Falko-Merlin (German) and RULEC-GEC (Russian).
Further analysis demonstrates that our data construction method is
complementary to rule-based approaches.

本文提出了一种通用且与语言无关的多语言语法纠错方法，通过预训练的跨语言语言模型和英语与目标语言之间的平行翻译数据，构建了多样化的平行语法纠错数据，并利用该数据训练语法纠错模型，在不同语言的三个基准测试中取得了最先进的结果。