Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation
This work addresses grammatical error correction for Arabic, a domain-specific problem, but is incremental as it builds on existing Transformer methods and focuses on a less-studied language.
The paper tackled grammatical error correction and detection in Arabic, a morphologically rich language with limited prior research, by introducing Transformer-based models and showing that using grammatical error detection as auxiliary input improves correction performance across three datasets, achieving state-of-the-art results on two shared task datasets and establishing a benchmark on a new dataset.
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.