@inproceedings{white-rozovskaya-2020-comparative,
    title = "A Comparative Study of Synthetic Data Generation Methods for Grammatical Error Correction",
    author = "White, Max  and
      Rozovskaya, Alla",
    editor = "Burstein, Jill  and
      Kochmar, Ekaterina  and
      Leacock, Claudia  and
      Madnani, Nitin  and
      Pil{\'a}n, Ildik{\'o}  and
      Yannakoudakis, Helen  and
      Zesch, Torsten",
    booktitle = "Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications",
    month = jul,
    year = "2020",
    address = "Seattle, WA, USA {\textrightarrow} Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.bea-1.21/",
    doi = "10.18653/v1/2020.bea-1.21",
    pages = "198--208",
    abstract = "Grammatical Error Correction (GEC) is concerned with correcting grammatical errors in written text. Current GEC systems, namely those leveraging statistical and neural machine translation, require large quantities of annotated training data, which can be expensive or impractical to obtain. This research compares techniques for generating synthetic data utilized by the two highest scoring submissions to the restricted and low-resource tracks in the BEA-2019 Shared Task on Grammatical Error Correction."
}