@inproceedings{jamal-etal-2022-arabic,
    title = "On The {A}rabic Dialects' Identification: Overcoming Challenges of Geographical Similarities Between {A}rabic dialects and Imbalanced Datasets",
    author = "Jamal, Salma  and
      .Kassem, Aly M  and
      Mohamed, Omar  and
      Ashraf, Ali",
    editor = "Bouamor, Houda  and
      Al-Khalifa, Hend  and
      Darwish, Kareem  and
      Rambow, Owen  and
      Bougares, Fethi  and
      Abdelali, Ahmed  and
      Tomeh, Nadi  and
      Khalifa, Salam  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.wanlp-1.49/",
    doi = "10.18653/v1/2022.wanlp-1.49",
    pages = "458--463",
    abstract = "Arabic is one of the world{'}s richest languages, with a diverse range of dialects based on geographical origin. In this paper, we present a solution to tackle subtask 1 (Country-level dialect identification) of the Nuanced Arabic Dialect Identification (NADI) shared task 2022 achieving third place with an average macro F1 score between the two test sets of 26.44{\%}. In the preprocessing stage, we removed the most common frequent terms from all sentences across all dialects, and in the modeling step, we employed a hybrid loss function approach that includes Weighted cross entropy loss and Vector Scaling(VS) Loss. On test sets A and B, our model achieved 35.68{\%} and 17.192{\%} Macro F1 scores, respectively."
}