Fine-Grained Arabic Dialect Identification
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.
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