@InProceedings{malmasi-EtAl:2016:VarDial3,
  author    = {Malmasi, Shervin  and  Zampieri, Marcos  and  Ljube\v{s}i\'{c}, Nikola  and  Nakov, Preslav  and  Ali, Ahmed  and  Tiedemann, J\"{o}rg},
  title     = {Discriminating between Similar Languages and Arabic Dialect Identification: A Report on the Third DSL Shared Task},
  booktitle = {Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1--14},
  abstract  = {We present the results of the third edition of the Discriminating between
	Similar Languages (DSL) shared task, which was organized as part of the
	VarDial'2016 workshop at COLING'2016. The challenge offered two subtasks:
	subtask 1 focused on the identification of very similar languages and language
	varieties in newswire texts, whereas subtask 2 dealt with Arabic dialect
	identification in speech transcripts. A total of 37 teams registered to
	participate in the task, 24 teams submitted test results, and 20 teams also
	wrote system description papers.
	High-order character n-grams were the most successful feature, and the best
	classification approaches included traditional supervised learning methods such
	as SVM, logistic regression, and language models, while deep learning
	approaches did not perform very well.},
  url       = {http://aclweb.org/anthology/W16-4801}
}