Elamur Research⌕
☰
home / papers / 2026 / Learning the Arabic Dialect Continuum as a Continuous Space: A Regression Approach to Speaker Origin Prediction

Learning the Arabic Dialect Continuum as a Continuous Space: A Regression Approach to Speaker Origin Prediction

Summary

Arabic dialect identification is usually treated as a choice among fixed classes, such as countries or regions. Sociolinguistics, however, describes dialects as gradual continua. The authors instead predict a speaker's origin from audio as latitude–longitude coordinates. They use the ARCADE radio corpus: 2,329 clips from 19 countries and 46 cities. Their model combines frame-level XLS-R-300M and Whisper-large-v3 features with phonotactic descriptors. A Transformer encoder processes this fused input, and a learnable query pools it by attention. Training minimizes great-circle distance on the sphere, with auxiliary country and city heads alongside. Evaluation uses 5-fold GroupKFold split by source recording to prevent leakage. Under this protocol, the median localization error is 481.2 km. A Mantel test on the latent space supports the continuum hypothesis. A city-masking protocol tests cities never seen in training, where the error grows noticeably. The work offers a distance-based, comparable metric for dialect geolocation and states plainly how much room for improvement remains.

Key points

  • Data and setup: ARCADE corpus, 2,329 speech samples, 19 countries, 46 cities. Labels are city centroids taken from self-reported origin.
  • Coordinate accuracy (pooled): mean error 901.5 km, median 481.2 km. 51.7% of predictions fall within 500 km, 69.5% within 1000 km and 90.9% within 2500 km.
  • Auxiliary heads and calibration: the country head reaches 64.5% accuracy and the city head 45.2%. ECE is about 0.11 for country and 0.14 for city, which the authors judge not yet ready for production.
  • Continuum and selective prediction: the Mantel test gives r = 0.131, p ≈ 10⁻⁴ over 10,000 permutations. Keeping only the 80% most confident predictions lowers the mean error to 749.2 km and the median to 350.5 km.
  • Zero-shot cities: two cities per fold are masked from training. Their mean error is 1173.3 km, a 1.32× degradation. Isolated cities (Tripoli, Aleppo, Sharjah) suffer most, while cities with same-region neighbours in training (Tunis, El Obeid) degrade only mildly.

Interesting ideas

  • I1. Loss that matches the target's geometry: predict a continuous quantity with a loss defined in its true space (great-circle distance), rather than binning it or regressing on a flat plane; this removes quantization and distortion errors. — For Mova (world): numeric states and measures, such as quantities, distances and times, could be scored by a distance natural to each unit instead of exact match. Near-misses would then be penalized gradually.
  • I2. Splitting by source: cross-validation folds are grouped by the original recording, so pieces of one source never appear on both sides. — For Mova (learn): when training on silver data agreed by witness parsers, folds should be split by source document. Otherwise style and vocabulary from one text inflate the scores.
  • I3. Masking whole categories: remove entire categories from training but keep them in validation, so true zero-shot ability is measured separately from closed-set accuracy. — For Mova (learn): hold out whole VerbNet classes or whole fable collections during training. This would show whether frames and answers generalize or only memorize.
  • I4. Permutation test on learned structure: a Mantel test checks whether distances in the learned latent space follow a known external distance, with significance from shuffled pairings. — For Mova (world): test whether distances between learned representations track WordNet or knowledge-graph distances. Shuffled pairings serve as a ready-made negative control.
  • I5. Confidence-based abstention: let the system decline its least confident 20% of cases, trading coverage for a clear drop in error. — For Mova (answer): the readers could abstain or flag an answer when parse witnesses or frame readings disagree. This would report accuracy at a fixed coverage instead of forcing an answer on every question.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2607.19751 · arXiv 2607.19751

@misc{khadraoui2026learning,
  title = {{Learning the Arabic Dialect Continuum as a Continuous Space: A Regression Approach to Speaker Origin Prediction}},
  author = {Khadraoui, Mohamed Aziz and Ammar, Adel and Benjdira, Bilel and Khan, Zahid and Turki, Skander and Boulila, Wadii},
  year = {2026},
  doi = {10.48550/arxiv.2607.19751},
  eprint = {2607.19751},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2607.19751}
}

References

28 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Abdelali, Ahmed, Hamdy Mubarak, et al. 2021. QADI: Arabic dialect identification in the wild. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 1–10. find our card
  2. Abdul-Mageed, Muhammad, AbdelRahim Elmadany, et al. 2021. Arbert & marbert: Deep bidirectional transformers for arabic. In Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers), pages 7088–7105. find our card
  3. Abdul-Mageed, Muhammad, AbdelRahim Elmadany, et al. 2024. NADI 2024: The fifth nuanced Arabic dialect identification shared task. In Proceedings of ArabicNLP 2024. find
  4. Ali, Ahmed, Najim Dehak, Patrick Cardinal, Sameer Khurana, Sree Harsha Yella, James Glass, Peter Bell, and Steve Renals. 2016. Automatic dialect detection in Arabic broadcast speech. In Proc. Interspeech 2016, pages 2934–2938. find
  5. Antoun, Wissam, Fady Baly, and Hazem Hajj. 2020. AraBERT: Transformer-based model for Arabic language understanding. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, pages 9–15. find our card
  6. Babu, Arun, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, and Michael Auli. 2022. XLS-R: Self-supervised cross-lingual speech representation learning at scale. In Proc. Interspeech 2022, pages 2278–2282. find our card
  7. Bouamor, Houda, Nizar Habash, Mohammad Salameh, Wajdi Zaghouani, Owen Rambow, et al. 2018. The MADAR Arabic dialect corpus and lexicon. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC), pages 3387–3396. find
  8. Chambers, J. K. and Peter Trudgill. 1998. Dialectology, 2 edition. Cambridge Textbooks in Linguistics. Cambridge University Press. find
  9. Elaraby, Mohamed and Muhammad Abdul-Mageed. 2018. Deep models for Arabic dialect identification on benchmarked data. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial), pages 263–274. find
  10. Elfardy, Heba and Mona Diab. 2013. Sentence level dialect identification in Arabic. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 456–461. find
  11. Habash, Nizar, Mona T Diab, and Owen Rambow. 2012. Conventional orthography for dialectal Arabic. In Proceedings of LREC, pages 711–718. find our card
  12. Hamed, Injy et al. 2025. A survey of code-switched arabic nlp. arXiv preprint arXiv:2501.13419. ↗
  13. Heeringa, Wilbert and John Nerbonne. 2001. Dialect areas and dialect continua. Language Variation and Change, 13(3):375–400. find
  14. Keleg, Amr, Sharon Goldwater, and Walid Magdy. 2023. ALDi: Quantifying the Arabic level of dialectness of text. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10597–10611, Association for Computational Linguistics. find
  15. Khurana, Sameer, Maryam Najafian, Ahmed Ali, Tuka Al Hanai, Yonatan Belinkov, and James Glass. 2017. Multi-view dimensionality reduction for dialect identification of Arabic broadcast speech. In Proc. Interspeech 2017, pages 2596–2600. find
  16. Nacar, Omer, Serry Sibaee, Adel Ammar, Yasser Alhabashi, Nadia Samer Sibai, Yara Farouk Ahmed, Ahmed Saud Alqusaiyer, Sulieman Mahmoud AlMahmoud, Abdulrhman Mamdoh Mukhaniq, Lubaba Raed, Sulaiman Mohammed Alatwah, Waad Nasser Alqahtani, Yousif Abdulmajeed Alnasser, Mohamed Aziz Khadraoui, and Wadii Boulila. 2026. ARCADE: A city-scale corpus for fine-grained Arabic dialect tagging. arXiv preprint arXiv:2601.02209. ↗
  17. Nerbonne, John. 2009. Data-driven dialectology. Language and Linguistics Compass, 3(1):175–198. find
  18. Obeid, Ossama et al. 2020. CAMeL tools. In Proceedings of LREC, pages 7022–7032. find
  19. OpenStreetMap Foundation. 2024. Nominatim: Open-source geocoding with OpenStreetMap data. https://nominatim.org/. Accessed 21/07/2026. ↗
  20. Pasha, Arfath et al. 2014. MADAMIRA. In Proceedings of LREC, pages 1094–1101. find
  21. Radford, Alec, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. 2023. Robust speech recognition via large-scale weak supervision. In Proceedings of the 40th International Conference on Machine Learning (ICML), pages 28492–28518. find our card
  22. Sha’ban, Sanad and Nizar Habash. 2025. The arabic generality score: Another dimension of modeling arabic dialectness. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 29990–30001. find
  23. Shon, Suwon, Ahmed Ali, Younes Samih, Hamdy Mubarak, and James Glass. 2020. ADI17: A fine-grained Arabic dialect identification dataset. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8244–8248. find our card
  24. Soliman, Abu Bakr et al. 2017. AraVec. In Procedia Computer Science, volume 117, pages 256–265. find
  25. Talafha, Bashar et al. 2024. Casablanca. arXiv preprint arXiv:2410.04527. ↗
  26. Vivanco Cepeda, Vicente, Gaurav Kumar Nayak, and Mubarak Shah. 2023. GeoCLIP: Clip-inspired alignment between locations and images for effective worldwide geo-localization. In Advances in Neural Information Processing Systems (NeurIPS), volume 36, pages 8690–8701. find
  27. Zaidan, Omar F and Chris Callison-Burch. 2011. The Arabic online commentary dataset. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 37–41. find
  28. Zaidan, Omar F and Chris Callison-Burch. 2014. Arabic dialect identification. Computational Linguistics, 40(1):171–202. find our card

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/khadraoui-2026-learning-arabic-dialect-continuum-continuous/