@inproceedings{hamed-etal-2025-lahjawi,
    title = "Lahjawi: {A}rabic Cross-Dialect Translator",
    author = "Hamed, Mohamed Motasim  and
      Hreden, Muhammad  and
      Hennara, Khalil  and
      Aldallal, Zeina  and
      Chrouf, Sara  and
      AlModhayan, Safwan",
    editor = "Ezzini, Saad  and
      Alami, Hamza  and
      Berrada, Ismail  and
      Benlahbib, Abdessamad  and
      El Mahdaouy, Abdelkader  and
      Lamsiyah, Salima  and
      Derrouz, Hatim  and
      Haddad Haddad, Amal  and
      Jarrar, Mustafa  and
      El-Haj, Mo  and
      Mitkov, Ruslan  and
      Rayson, Paul",
    booktitle = "Proceedings of the 4th Workshop on Arabic Corpus Linguistics (WACL-4)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2025.wacl-1.2/",
    pages = "12--24",
    abstract = "In this paper, we explore the rich diversity of Arabic dialects by introducing a suite of pioneering models called Lahjawi. The primary model, Lahjawi-D2D, is the first designed for cross-dialect translation among 15 Arabic dialects. Furthermore, we introduce Lahjawi-D2MSA, a model designed to convert any Arabic dialect into Modern Standard Arabic (MSA). Both models are fine-tuned versions of Kuwain-1.5B an in-house built small language model, tailored for Arabic linguistic characteristics. We provide a detailed overview of Lahjawi`s architecture and training methods, along with a comprehensive evaluation of its performance. The results demonstrate Lahjawi`s success in preserving meaning and style, with BLEU scores of 9.62 for dialect-to-MSA and 9.88 for dialect-to- dialect tasks. Additionally, human evaluation reveals an accuracy score of 58{\%} and a fluency score of 78{\%}, underscoring Lahjawi`s robust handling of diverse dialectal nuances. This research sets a foundation for future advancements in Arabic NLP and cross-dialect communication technologies."
}