  Grammatical error correction (GEC) is a well-explored problem in English with
many existing models and datasets. However, research on GEC in morphologically
rich languages has been limited due to challenges such as data scarcity and
language complexity. In this paper, we present the first results on Arabic GEC
using two newly developed Transformer-based pretrained sequence-to-sequence
models. We also define the task of multi-class Arabic grammatical error
detection (GED) and present the first results on multi-class Arabic GED. We
show that using GED information as an auxiliary input in GEC models improves
GEC performance across three datasets spanning different genres. Moreover, we
also investigate the use of contextual morphological preprocessing in aiding
GEC systems. Our models achieve SOTA results on two Arabic GEC shared task
datasets and establish a strong benchmark on a recently created dataset. We
make our code, data, and pretrained models publicly available.
