  Although the prediction of dialects is an important language processing task,
with a wide range of applications, existing work is largely limited to
coarse-grained varieties. Inspired by geolocation research, we propose the
novel task of Micro-Dialect Identification (MDI) and introduce MARBERT, a new
language model with striking abilities to predict a fine-grained variety (as
small as that of a city) given a single, short message. For modeling, we offer
a range of novel spatially and linguistically-motivated multi-task learning
models. To showcase the utility of our models, we introduce a new, large-scale
dataset of Arabic micro-varieties (low-resource) suited to our tasks. MARBERT
predicts micro-dialects with 9.9% F1, ~76X better than a majority class
baseline. Our new language model also establishes new state-of-the-art on
several external tasks.
