@inproceedings{bowen-etal-2017-effect,
    title = "The Effect of Error Rate in Artificially Generated Data for Automatic Preposition and Determiner Correction",
    author = "Bowen, Fraser  and
      Dehdari, Jon  and
      van Genabith, Josef",
    editor = "Derczynski, Leon  and
      Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/W17-4410/",
    doi = "10.18653/v1/W17-4410",
    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 {\textquotedblleft}real{\textquotedblright} 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."
}