@InProceedings{goutte-leger:2017:BEA,
  author    = {Goutte, Cyril  and  L\'{e}ger, Serge},
  title     = {Exploring Optimal Voting in Native Language Identification},
  booktitle = {Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {367--373},
  abstract  = {We describe the submissions entered by the National Research Council
	Canada in the NLI-2017 evaluation. We mainly explored the use of
	voting, and various ways to optimize the choice and number of voting
	systems.  We also explored the use of features that rely on no
	linguistic preprocessing. Long ngrams of characters obtained from raw
	text turned out to yield the best performance on all textual input
	(written essays and speech transcripts). Voting ensembles turned out
	to produce small performance gains, with little difference between the
	various optimization strategies we tried. Our top systems achieved
	accuracies of 87% on the \essay\ track, 84% on the
	\speech\ track, and close to 92% by combining essays, speech and
	i-vectors in the \fusion\ track.},
  url       = {http://www.aclweb.org/anthology/W17-5041}
}