@InProceedings{chen-EtAl:2017:NMT,
  author    = {Chen, Boxing  and  Cherry, Colin  and  Foster, George  and  Larkin, Samuel},
  title     = {Cost Weighting for Neural Machine Translation Domain Adaptation},
  booktitle = {Proceedings of the First Workshop on Neural Machine Translation},
  month     = {August},
  year      = {2017},
  address   = {Vancouver},
  publisher = {Association for Computational Linguistics},
  pages     = {40--46},
  abstract  = {In this paper, we propose a new domain adaptation technique for neural machine
	translation called cost weighting, which is appropriate for adaptation
	scenarios in which a small in-domain data set and a large general-domain data
	set are available. Cost weighting incorporates a domain classifier into the
	neural machine translation training algorithm, using features derived from the
	encoder representation in order to distinguish in-domain from out-of-domain
	data. Classifier probabilities are used to weight sentences according to their
	domain similarity when updating the parameters of the neural translation model.
	We compare cost weighting to two traditional domain adaptation techniques
	developed for statistical machine translation: data selection and sub-corpus
	weighting. Experiments on two large-data tasks show that both the traditional
	techniques and our novel proposal lead to significant gains, with cost
	weighting outperforming the traditional methods.},
  url       = {http://www.aclweb.org/anthology/W17-3205}
}