@InProceedings{junczysdowmunt-EtAl:2018:N18-1,
  author    = {Junczys-Dowmunt, Marcin  and  Grundkiewicz, Roman  and  Guha, Shubha  and  Heafield, Kenneth},
  title     = {Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {595--606},
  abstract  = {Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10\% M$\^{}2$ on the CoNLL-2014 benchmark and 5.9\% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2\% on the CoNLL-2014 benchmark and by 4\% on JFLEG.},
  url       = {http://www.aclweb.org/anthology/N18-1055}
}