@inproceedings{farouk-zakaria-elshabrawy-etal-2026-parsing,
    title = "Parsing {A}rabic Dialects Revisited: New Benchmarks, Models, and Insights",
    author = "Farouk Zakaria Elshabrawy, Ahmed  and
      Inoue, Go  and
      AbuOdeh, Muhammed  and
      Habash, Nizar",
    editor = "Al-Khalifa, Hend  and
      El-Haj, Mo  and
      Ezzini, Saad",
    booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.osact-1.12/",
    doi = "10.63317/3eyeu3k726ab",
    pages = "94--105",
    abstract = "Parsing dialectal Arabic remains underexplored, with limited progress over the past two decades. Existing Modern Standard Arabic (MSA) parsers perform poorly on dialectal data, motivating the need for dialect-specific approaches. We revisit this task using modern neural models and present new results on Egyptian and Gulf Arabic dependency parsing. We demonstrate that even small amounts of dialectal training data yield substantial improvements in parsing accuracy. Our contributions include: (1) introducing a new annotated dataset for Gulf Arabic, (2) releasing a state-of-the-art multi-variety Arabic parser, and (3) employing dialect identification as a diagnostic tool to better understand how training data affects parsing performance across dialects and test sets."
}