@InProceedings{yannakoudakis-EtAl:2017:EMNLP2017,
  author    = {Yannakoudakis, Helen  and  Rei, Marek  and  Andersen, {\O}istein E.  and  Yuan, Zheng},
  title     = {Neural Sequence-Labelling Models for Grammatical Error Correction},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2795--2806},
  abstract  = {We propose an approach to N -best list re-
	ranking using neural sequence-labelling
	models. We train a compositional model
	for error detection that calculates the prob-
	ability of each token in a sentence being
	correct or incorrect, utilising the full sen-
	tence as context. Using the error detec-
	tion model, we then re-rank the N best
	hypotheses generated by statistical ma-
	chine translation systems. Our approach
	achieves state-of-the-art results on error
	correction for three different datasets, and
	it has the additional advantage of only us-
	ing a small set of easily computed features
	that require no linguistic input.},
  url       = {https://www.aclweb.org/anthology/D17-1297}
}