@inproceedings{hossain-etal-2025-enhancing,
    title = "Enhancing Dialectal {A}rabic Intent Detection through Cross-Dialect Multilingual Input Augmentation",
    author = "Hossain, Shehenaz  and
      Shammary, Fouad  and
      Shammary, Bahaulddin  and
      Afli, Haithem",
    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-1.5/",
    pages = "44--49",
    abstract = "Addressing the challenges of Arabic intent detection amid extensive dialectal variation, this study presents a crossdialtectal, multilingual approach for classifying intents in banking and migration contexts. By augmenting dialectal inputs with Modern Standard Arabic (MSA) and English translations, our method leverages cross-lingual context to improve classification accuracy. We evaluate single-input (dialect-only), dual-input (dialect + MSA), and triple-input (dialect + MSA + English) models, applying language-specific tokenization for each. Results demonstrate that, in the migration dataset, our model achieved an accuracy gain of over 50{\%} on Tunisian dialect, increasing from 43.3{\%} with dialect-only input to 94{\%} with the full multilingual setup. Similarly, in the PAL (Palestinian dialect) dataset, accuracy improved from 87.7{\%} to 93.5{\%} with translation augmentation, reflecting a gain of 5.8 percentage points. These findings underscore the effectiveness of our approach for intent detection across various Arabic dialects."
}