@inproceedings{alharbi-etal-2025-ador,
    title = "{ADOR}: Dataset for {A}rabic Dialects in Hotel Reviews: A Human Benchmark for Sentiment Analysis",
    author = "Alharbi, Maram I.  and
      Ezzini, Saad  and
      Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Mitkov, Ruslan",
    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-1.17/",
    pages = "187--191",
    abstract = "Arabic machine translation remains a fundamentally challenging task, primarily due to the lack of comprehensive annotated resources. This study evaluates the performance of Meta{'}s NLLB-200 model in translating Modern Standard Arabic (MSA) into three regional dialects: Saudi, Maghribi, and Egyptian Arabic using a manually curated dataset of hotel reviews. We applied a multi-criteria human annotation framework to assess translation correctness, dialect accuracy, and sentiment and aspect preservation. Our analysis reveals significant variation in translation quality across dialects. While sentiment and aspect preservation were generally high, dialect accuracy and overall translation fidelity were inconsistent. For Saudi Arabic, over 95{\%} of translations required human correction, highlighting systemic issues. Maghribi outputs demonstrated better dialectal authenticity, while Egyptian translations achieved the highest reliability with the lowest correction rate and fewest multi-criteria failures. These results underscore the limitations of current multilingual models in handling informal Arabic varieties and highlight the importance of dialect-sensitive evaluation."
}