  This paper presents a simple recipe to train state-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 step on cLang-8
with the off-the-shelf language models yields further accuracy improvements
over an already top-performing gT5 model for English.
