@InProceedings{malmasi-zampieri:2017:VarDial2,
  author    = {Malmasi, Shervin  and  Zampieri, Marcos},
  title     = {Arabic Dialect Identification Using iVectors and ASR Transcripts},
  booktitle = {Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {178--183},
  abstract  = {This paper presents the systems submitted by the MAZA team to the Arabic
	Dialect Identification (ADI) shared task at the VarDial Evaluation Campaign
	2017. The goal of the task is to evaluate computational models to identify the
	dialect of Arabic utterances using both audio and text transcriptions. The ADI
	shared task dataset included Modern Standard Arabic (MSA) and four Arabic
	dialects: Egyptian, Gulf, Levantine, and North-African. The three systems
	submitted by MAZA are based on combinations of multiple machine learning
	classifiers arranged as (1) voting ensemble; (2) mean probability ensemble; (3)
	meta-classifier. The best results were obtained by the meta-classifier
	achieving 71.7% accuracy, ranking second among the six teams which participated
	in the ADI shared task.},
  url       = {http://www.aclweb.org/anthology/W17-1222}
}