%0 Conference Proceedings %T A Simple Recipe for Multilingual Grammatical Error Correction %A Rothe, Sascha %A Mallinson, Jonathan %A Malmi, Eric %A Krause, Sebastian %A Severyn, Aliaksei %Y Zong, Chengqing %Y Xia, Fei %Y Li, Wenjie %Y Navigli, Roberto %S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F rothe-etal-2021-simple %X This paper presents a simple recipe to trainstate-of-the-art multilingual Grammatical Error Correction (GEC) models. We achieve this by first proposing a language-agnostic method to generate a large number of synthetic examples. The second ingredient is to use large-scale multilingual language models (up to 11B parameters). Once fine-tuned on language-specific supervised sets we surpass the previous state-of-the-art results on GEC benchmarks in four languages: English, Czech, German and Russian. Having established a new set of baselines for GEC, we make our results easily reproducible and accessible by releasing a CLANG-8 dataset. It is produced by using our best model, which we call gT5, to clean the targets of a widely used yet noisy Lang-8 dataset. cLang-8 greatly simplifies typical GEC training pipelines composed of multiple fine-tuning stages – we demonstrate that performing a single fine-tuning stepon cLang-8 with the off-the-shelf language models yields further accuracy improvements over an already top-performing gT5 model for English. %R 10.18653/v1/2021.acl-short.89 %U https://aclanthology.org/2021.acl-short.89 %U https://doi.org/10.18653/v1/2021.acl-short.89 %P 702-707