@InProceedings{criscuolo-aluisio:2017:VarDial,
  author    = {Criscuolo, Marcelo  and  Aluisio, Sandra Maria},
  title     = {Discriminating between Similar Languages with Word-level Convolutional Neural Networks},
  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     = {124--130},
  abstract  = {Discriminating between Similar Languages (DSL) is a challenging task addressed
	at the VarDial Workshop series. We report on our participation in the DSL
	shared task with a two-stage system. In the first stage, character n-grams are
	used to separate language groups, then specialized classifiers distinguish
	similar language varieties. We have conducted experiments with three system
	configurations and submitted one run for each. Our main approach is a
	word-level convolutional neural network (CNN) that learns task-specific vectors
	with minimal text preprocessing. We also experiment with multi-layer perceptron
	(MLP) networks and another hybrid configuration. Our best run achieved an
	accuracy of 90.76%, ranking 8th among 11 participants and getting very close to
	the system that ranked first (less than 2 points). Even though the CNN model
	could not achieve the best results, it still makes a viable approach to
	discriminating between similar languages.},
  url       = {http://www.aclweb.org/anthology/W17-1215}
}