We present Camel Morph MSA, the largest open-source Modern Standard Arabic morphological analyzer and generator. Camel Morph MSA has over 100K lemmas, and includes rarely modeled morphological features of Modern Standard Arabic with Classical Arabic origins. Camel Morph MSA can produce ∼1.45B analyses and ∼535M unique diacritizations, almost an order of magnitude larger than SAMA (Maamouri et al., 2010c), in addition to having ∼36% less OOV rate than SAMA on a 10B word corpus. Furthermore, Camel Morph MSA fills the gaps of many lemma paradigms by modeling linguistic phenomena consistently. Camel Morph MSA seamlessly integrates with the Camel Tools Python toolkit (Obeid et al., 2020), ensuring ease of use and accessibility.

Grammatical error correction (GEC) is a wellexplored 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.1