@inproceedings{khered-etal-2025-multi,
    title = "A Multi-Task Learning Approach to Dialectal {A}rabic Identification and Translation to {M}odern {S}tandard {A}rabic",
    author = "Khered, Abdullah  and
      Benkhedda, Youcef  and
      Batista-Navarro, Riza",
    editor = "Estevanell-Valladares, Ernesto Luis  and
      Picazo-Izquierdo, Alicia  and
      Ranasinghe, Tharindu  and
      Mikaberidze, Besik  and
      Ostermann, Simon  and
      Gurgurov, Daniil  and
      Mueller, Philipp  and
      Borg, Claudia  and
      {\v{S}}imko, Mari{\'a}n",
    booktitle = "Proceedings of the First Workshop on Advancing NLP for Low-Resource Languages",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.lowresnlp-1.4/",
    pages = "21--31",
    abstract = "Translating Dialectal Arabic (DA) into Modern Standard Arabic (MSA) is a complex task due to the linguistic diversity and informal nature of dialects, particularly in social media texts. To improve translation quality, we propose a Multi-Task Learning (MTL) framework that combines DA-MSA translation as the primary task and dialect identification as an auxiliary task. Additionally, we introduce LahjaTube, a new corpus containing DA transcripts and corresponding MSA and English translations, covering four major Arabic dialects: Egyptian (EGY), Gulf (GLF), Levantine (LEV), and Maghrebi (MGR), collected from YouTube. We evaluate AraT5 and AraBART on the Dial2MSA-Verified dataset under Single-Task Learning (STL) and MTL setups. Our results show that adopting the MTL framework and incorporating LahjaTube into the training data improve the translation performance, leading to a BLEU score improvement of 2.65 points over baseline models."
}