@InProceedings{chollampatt-ng:2017:BEA,
  author    = {Chollampatt, Shamil  and  Ng, Hwee Tou},
  title     = {Connecting the Dots: Towards Human-Level Grammatical Error Correction},
  booktitle = {Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {327--333},
  abstract  = {We build a grammatical error correction (GEC) system primarily based on the
	state-of-the-art statistical machine translation (SMT) approach, using
	task-specific features and tuning, and further enhance it with the modeling
	power of neural network joint models. The SMT-based system is weak in
	generalizing beyond patterns seen during training and lacks granularity below
	the word level. To address this issue, we incorporate a character-level SMT
	component targeting the misspelled words that the original SMT-based system
	fails to correct. Our final system achieves 53.14% F 0.5 score on the benchmark
	CoNLL-2014 test set, an improvement of 3.62% F 0.5 over the best previous
	published score.},
  url       = {http://www.aclweb.org/anthology/W17-5037}
}