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Prediction of language varieties and dialects is an important languageprocessing task, with a wide range of applications. For Arabic, the nativetongue of ~ 300 million people, most varieties remain unsupported. To ease thisbottleneck, we present a very large scale dataset covering 319 cities from all21 Arab countries. We introduce a hierarchical attention multi-task learning(HA-MTL) approach for dialect identification exploiting our data at the city,state, and country levels. We also evaluate use of BERT on the three tasks,comparing it to the MTL approach. We benchmark and release our data and models.

DiaNet: BERT and Hierarchical Attention Multi-Task Learning of Fine-Grained Dialect