  Grammatical Error Detection (GED) methods rely heavily on human annotated
error corpora. However, these annotations are unavailable in many low-resource
languages. In this paper, we investigate GED in this context. Leveraging the
zero-shot cross-lingual transfer capabilities of multilingual pre-trained
language models, we train a model using data from a diverse set of languages to
generate synthetic errors in other languages. These synthetic error corpora are
then used to train a GED model. Specifically we propose a two-stage fine-tuning
pipeline where the GED model is first fine-tuned on multilingual synthetic data
from target languages followed by fine-tuning on human-annotated GED corpora
from source languages. This approach outperforms current state-of-the-art
annotation-free GED methods. We also analyse the errors produced by our method
and other strong baselines, finding that our approach produces errors that are
more diverse and more similar to human errors.
