@InProceedings{medvedeva-kroon-plank:2017:VarDial,
  author    = {Medvedeva, Maria  and  Kroon, Martin  and  Plank, Barbara},
  title     = {When Sparse Traditional Models Outperform Dense Neural Networks: the Curious Case of Discriminating between Similar Languages},
  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     = {156--163},
  abstract  = {We present the results of our participation in the VarDial 4 shared task on
	discriminating closely related languages. Our submission includes simple
	traditional models using linear support vector machines (SVMs) and a neural
	network (NN). The main idea was to leverage language group information. We did
	so with a two-layer approach in the traditional model and a multi-task
	objective in the neural network case. Our results confirm earlier findings:
	simple traditional models outperform neural networks consistently for this
	task, at least given the amount of systems we could examine in the available
	time. Our two-layer linear SVM ranked 2nd in the shared task.},
  url       = {http://www.aclweb.org/anthology/W17-1219}
}