@InProceedings{bryant-briscoe:2018:W18-05,
  author    = {Bryant, Christopher  and  Briscoe, Ted},
  title     = {Language Model Based Grammatical Error Correction without Annotated Training Data},
  booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {247--253},
  abstract  = {Since the end of the CoNLL-2014 shared task on grammatical error correction (GEC), research into language model (LM) based approaches to GEC has largely stagnated. In this paper, we re-examine LMs in GEC and show that it is entirely possible to build a simple system that not only requires minimal annotated data ($\sim$1000 sentences), but is also fairly competitive with several state-of-the-art systems. This approach should be of particular interest for languages where very little annotated training data exists, although we also hope to use it as a baseline to motivate future research.},
  url       = {http://www.aclweb.org/anthology/W18-0529}
}