@InProceedings{sari-rifqifatchurrahman-dwiastuti:2017:BEA,
  author    = {Sari, Yunita  and  Rifqi Fatchurrahman, Muhammad  and  Dwiastuti, Meisyarah},
  title     = {A Shallow Neural Network for Native Language Identification with Character N-grams},
  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     = {249--254},
  abstract  = {This paper describes the systems submitted by GadjahMada team to the Native
	Language Identification (NLI) Shared Task 2017. Our models used a continuous
	representation of character n-grams which are learned jointly with feed-forward
	neural network classifier. Character n-grams have been proved to be effective
	for style-based identification tasks including NLI. Results on the test set
	demonstrate that the proposed model performs very well on essay and fusion
	tracks by obtaining more than 0.8 on both F-macro score and accuracy.},
  url       = {http://www.aclweb.org/anthology/W17-5027}
}