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We present state-of-the-art results on morphosyntactic tagging acrossdifferent varieties of Arabic using fine-tuned pre-trained transformer languagemodels. Our models consistently outperform existing systems in Modern StandardArabic and all the Arabic dialects we study, achieving 2.6% absoluteimprovement over the previous state-of-the-art in Modern Standard Arabic, 2.8%in Gulf, 1.6% in Egyptian, and 8.3% in Levantine. We explore different trainingsetups for fine-tuning pre-trained transformer language models, includingtraining data size, the use of external linguistic resources, and the use ofannotated data from other dialects in a low-resource scenario. Our results showthat strategic fine-tuning using datasets from other high-resource dialects isbeneficial for a low-resource dialect. Additionally, we show that high-qualitymorphological analyzers as external linguistic resources are beneficialespecially in low-resource settings.

Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects