@inproceedings{inoue-etal-2022-morphosyntactic,
    title = "Morphosyntactic Tagging with Pre-trained Language Models for {A}rabic and its Dialects",
    author = "Inoue, Go  and
      Khalifa, Salam  and
      Habash, Nizar",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.findings-acl.135/",
    doi = "10.18653/v1/2022.findings-acl.135",
    pages = "1708--1719",
    abstract = "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."
}