Abstract
Previous work on the problem of Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic (6-way classification). This paper presents the first results on a fine-grained dialect classification task covering 25 specific cities from across the Arab World, in addition to Standard Arabic – a very challenging task. We build several classification systems and explore a large space of features. Our results show that we can identify the exact city of a speaker at an accuracy of 67.9% for sentences with an average length of 7 words (a 9% relative error reduction over the state-of-the-art technique for Arabic dialect identification) and reach more than 90% when we consider 16 words. We also report on additional insights from a data analysis of similarity and difference across Arabic dialects.- Anthology ID:
- C18-1113
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1332–1344
- Language:
- URL:
- https://aclanthology.org/C18-1113
- DOI:
- Cite (ACL):
- Mohammad Salameh, Houda Bouamor, and Nizar Habash. 2018. Fine-Grained Arabic Dialect Identification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1332–1344, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Fine-Grained Arabic Dialect Identification (Salameh et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/C18-1113.pdf