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    <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.</abstract>
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%0 Conference Proceedings
%T Morphological Feature Extraction for Fine-Grained Sorani Kurdish Dialect Identification: A Hybrid Transformer-Linguistic Approach
%A Bharati, Soumedhik
%A Mandal, Shibam
%A Majumdar, Subham
%A Ghosh, Swarup Kr
%A Mondal, Sayani
%S Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F bharati-etal-2026-morphological
%X 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.
%U https://aclanthology.org/2026.abjadnlp-1.24/
%P 172-176

Markdown (Informal)

[Morphological Feature Extraction for Fine-Grained Sorani Kurdish Dialect Identification: A Hybrid Transformer-Linguistic Approach](https://aclanthology.org/2026.abjadnlp-1.24/) (Bharati et al., AbjadNLP 2026)

• Morphological Feature Extraction for Fine-Grained Sorani Kurdish Dialect Identification: A Hybrid Transformer-Linguistic Approach (Bharati et al., AbjadNLP 2026)

ACL

• Soumedhik Bharati, Shibam Mandal, Subham Majumdar, Swarup Kr Ghosh, and Sayani Mondal. 2026. Morphological Feature Extraction for Fine-Grained Sorani Kurdish Dialect Identification: A Hybrid Transformer-Linguistic Approach. In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 172–176, Rabat, Morocco. Association for Computational Linguistics.