@InProceedings{bjerva:2016:VarDial3,
  author    = {Bjerva, Johannes},
  title     = {Byte-based Language Identification with Deep Convolutional Networks},
  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     = {119--125},
  abstract  = {We report on our system for the shared task on discriminating between similar
	languages (DSL 2016).
	The system uses only byte representations in a deep residual network (ResNet).
	The system, named ResIdent, is trained only on the data released with the task
	(closed training).
	We obtain 84.88% accuracy on subtask A, 68.80% accuracy on subtask B1, and
	69.80% accuracy on subtask B2.
	A large difference in accuracy on development data can be observed with
	relatively minor changes in our network's architecture and hyperparameters.
	We therefore expect fine-tuning of these parameters to yield higher accuracies.},
  url       = {http://aclweb.org/anthology/W16-4816}
}