@inproceedings{salameh-etal-2018-fine,
    title = "Fine-Grained {A}rabic Dialect Identification",
    author = "Salameh, Mohammad  and
      Bouamor, Houda  and
      Habash, Nizar",
    editor = "Bender, Emily M.  and
      Derczynski, Leon  and
      Isabelle, Pierre",
    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/C18-1113/",
    pages = "1332--1344",
    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."
}