The paper describes the QCRI submissions to the shared task of automatic Arabic dialect classification into 5 Arabic variants, namely Egyptian, Gulf, Levantine, North-African (Maghrebi), and Modern Standard Arabic (MSA). The relatively small training set is automatically generated from an ASR system. To avoid over-fitting on such small data, we selected and designed features that capture the morphological essence of the different dialects. We submitted four runs to the Arabic sub-task. For all runs, we used a combined feature vector of character bigrams, trigrams, 4-grams, and 5-grams. We tried several machine-learning algorithms, namely Logistic Regression, Naive Bayes, Neural Networks, and Support Vector Machines (SVM) with linear and string kernels. Our submitted runs used SVM with a linear kernel. In the closed submission, we got the best accuracy of 0.5136 and the third best weighted Fl score, with a difference of less than 0.002 from the best system.
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