MADAR Twitter Corpus
A  large-scale collection of parallel sentences built to cover the dialects of 25 cities from the Arab World 
NYU Abu Dhabi
Fine-Grained Arabic Dialect Identification
machine translation, dialect identification
International Conference on Computational Linguistics
Mohammad Salameh, Houda Bouamor, Nizar Habash
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