  We present state-of-the-art results on morphosyntactic tagging across
different varieties of Arabic using fine-tuned pre-trained transformer language
models. Our models consistently outperform existing systems in Modern Standard
Arabic and all the Arabic dialects we study, achieving 2.6% absolute
improvement 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 training
setups for fine-tuning pre-trained transformer language models, including
training data size, the use of external linguistic resources, and the use of
annotated data from other dialects in a low-resource scenario. Our results show
that strategic fine-tuning using datasets from other high-resource dialects is
beneficial for a low-resource dialect. Additionally, we show that high-quality
morphological analyzers as external linguistic resources are beneficial
especially in low-resource settings.
