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

Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments