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.

该研究利用预训练转换器语言模型对不同形式的阿拉伯语进行形态句法标记，优于现有系统，在不同区域的阿拉伯语可获得显著改进，特别是在资源匮乏的情况下，利用其他高资源方言的数据进行微调，以及使用高质量的形态分析器作为外部语言资源是有益的。