  Prediction of language varieties and dialects is an important language
processing task, with a wide range of applications. For Arabic, the native
tongue of ~ 300 million people, most varieties remain unsupported. To ease this
bottleneck, we present a very large scale dataset covering 319 cities from all
21 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.

Computation and Language;Machine Learning