@inproceedings{nwesri-etal-2023-uot,
    title = "{U}o{T} at {NADI} 2023 shared task: Automatic {A}rabic Dialect Identification is Made Possible",
    author = "Nwesri, Abduslam F A  and
      Shinbir, Nabila A S  and
      Ebrahem, Hassan",
    editor = "Sawaf, Hassan  and
      El-Beltagy, Samhaa  and
      Zaghouani, Wajdi  and
      Magdy, Walid  and
      Abdelali, Ahmed  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Habash, Nizar  and
      Khalifa, Salam  and
      Keleg, Amr  and
      Haddad, Hatem  and
      Zitouni, Imed  and
      Mrini, Khalil  and
      Almatham, Rawan",
    booktitle = "Proceedings of ArabicNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.arabicnlp-1.64/",
    doi = "10.18653/v1/2023.arabicnlp-1.64",
    pages = "620--624",
    abstract = "In this paper we present our approach towards Arabic Dialect identification which was part of the Fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023). We tested several techniques to identify Arabic dialects. We obtained the best result by fine-tuning the pre-trained MARBERTv2 model with a modified training dataset. The training set was expanded by sorting tweets based on dialects, concatenating every two adjacent tweets, and adding them to the original dataset as new tweets. We achieved 82.87 on F1 score and we were at the seventh position among 16 participants."
}