@InProceedings{bowen-dehdari-vangenabith:2017:WNUT,
  author    = {Bowen, Fraser  and  Dehdari, Jon  and  Van Genabith, Josef},
  title     = {The Effect of Error Rate in Artificially Generated Data for Automatic Preposition and Determiner Correction},
  booktitle = {Proceedings of the 3rd Workshop on Noisy User-generated Text},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {68--76},
  abstract  = {In this research we investigate the impact of mismatches in the density and
	type of error between training and test data on a neural system correcting
	preposition and determiner errors. We use synthetically produced training data
	to control error density and type, and "real" error data for testing. Our
	results show it is possible to combine error types, although prepositions and
	determiners behave differently in terms of how much error should be
	artificially introduced into the training data in order to get the best
	results.},
  url       = {http://www.aclweb.org/anthology/W17-4410}
}