Name: MADAR Twitter Corpus
Description: A  large-scale collection of parallel sentences built to cover the dialects of 25 cities from the Arab World 
Provider: NYU Abu Dhabi
Paper Title: Fine-Grained Arabic Dialect Identification
Tasks: machine translation, dialect identification
Venue Name: International Conference on Computational Linguistics
Authors: Mohammad Salameh, Houda Bouamor, Nizar Habash
Abstract: Previous work on the problem of Arabic Dialect Identification typically targeted coarse-grained
  five dialect classes plus Standard Arabic (6-way classification). This paper presents the first
  results on a fine-grained dialect classification task covering 25 specific cities from across the Arab
  World, in addition to Standard Arabic – a very challenging task. We build several classification
  systems and explore a large space of features. Our results show that we can identify the exact
  city of a speaker at an accuracy of 67.9% for sentences with an average length of 7 words (a 9%
  relative error reduction over the state-of-the-art technique for Arabic dialect identification) and
  reach more than 90% when we consider 16 words. We also report on additional insights from a
  data analysis of similarity and difference across Arabic dialects