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
by using two newly developed Transformer-based pretrained sequence-to-sequence
models. We address 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 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 state-of-the-art results on two Arabic GEC shared tasks datasets
and establish a strong benchmark on a newly created dataset.

本文报道了使用两个基于 Transformer 的预训练序列到序列模型进行多类阿拉伯语语法错误检测的结果，研究表明，使用语法错误信息作为辅助输入可以提高语法纠错性能；此外，研究还探讨了上下文形态预处理在辅助语法纠错系统方面的应用，并在两个阿拉伯语 GEC 共享任务数据集上取得了最先进的结果。