@InProceedings{rei-EtAl:2017:BEA,
  author    = {Rei, Marek  and  Felice, Mariano  and  Yuan, Zheng  and  Briscoe, Ted},
  title     = {Artificial Error Generation with Machine Translation and Syntactic Patterns},
  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     = {287--292},
  abstract  = {Shortage of available training data is holding back progress in the area of
	automated error detection.
	This paper investigates two alternative methods for artificially generating
	writing errors, in order to create additional resources.
	We propose treating error generation as a machine translation task, where
	grammatically correct text is translated to contain errors.
	In addition, we explore a system for extracting textual patterns from an
	annotated corpus, which can then be used to insert errors into grammatically
	correct sentences.
	Our experiments show that the inclusion of artificially generated errors
	significantly improves error detection accuracy on both FCE and CoNLL 2014
	datasets.},
  url       = {http://www.aclweb.org/anthology/W17-5032}
}