@inproceedings{bharati-etal-2026-morphological,
    title = "Morphological Feature Extraction for Fine-Grained {S}orani {K}urdish Dialect Identification: A Hybrid Transformer-Linguistic Approach",
    author = "Bharati, Soumedhik  and
      Mandal, Shibam  and
      Majumdar, Subham  and
      Ghosh, Swarup Kr  and
      Mondal, Sayani",
    booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.abjadnlp-1.24/",
    pages = "172--176",
    abstract = "As reported, approximately 6 million people in Iraq and Iran speak in Sorani Kurdish, which exhibits substantial regional variation but lacks computational resources for dialect identification. We present the first fine-grained sub-dialect classification system for six Sorani varieties namely, Sulaymaniyah, Erbil, Iranian Sorani, Ardalani, Babani, and Mukriani. This investigation combines cross-lingual contextual embeddings (XLM-RoBERTa) with morphological features derived from explicit linguistic rules, including 24 patterns capturing verb prefixes, pronominal clitics, and definite markers. The suggested morphology-augmented XLM-R model has been trained on a unified dataset of 16,409 sentences without manual annotation, and achieves 91.91{\%} accuracy, outperforming pure transformers (91.79{\%}) and traditional machine learning baselines (SVM 86.41{\%}). Key ablation studies reveal that morphological features serve as effective regularizers for geographically proximate dialects."
}