@inproceedings{beltagy-etal-2020-arabic,
    title = "{A}rabic Dialect Identification Using {BERT}-Based Domain Adaptation",
    author = "Beltagy, Ahmad  and
      Abouelenin, Abdelrahman  and
      ElSherief, Omar",
    editor = "Zitouni, Imed  and
      Abdul-Mageed, Muhammad  and
      Bouamor, Houda  and
      Bougares, Fethi  and
      El-Haj, Mahmoud  and
      Tomeh, Nadi  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Fifth Arabic Natural Language Processing Workshop",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.wanlp-1.26/",
    pages = "262--267",
    abstract = "Arabic is one of the most important and growing languages in the world. With the rise of the social media giants like Twitter, Arabic spoken dialects have become more in use. In this paper we describe our effort and simple approach on the NADI Shared Task 1 that requires us to build a system to differentiate between different 21 Arabic dialects, we introduce a deep learning semisupervised fashion approach along with pre-processing that was reported on NADI shared Task 1 Corpus. Our system ranks 4th in NADI{'}s shared task competition achieving 23.09{\%} F1 macro average score with a very simple yet an efficient approach on differentiating between 21 Arabic Dialects given tweets."
}