@InProceedings{durrani-EtAl:2016:COLING,
  author    = {Durrani, Nadir  and  Sajjad, Hassan  and  Joty, Shafiq  and  Abdelali, Ahmed},
  title     = {A Deep Fusion Model for Domain Adaptation in Phrase-based MT},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3177--3187},
  abstract  = {We present a novel fusion model for domain adaptation in Statistical Machine
	Translation. Our model is based on the joint source-target 
	neural network Devlin et al., 2014, and is learned by fusing in- and out-domain
	models. The adaptation is performed by backpropagating errors from the output
	layer to the word embedding layer of each model, subsequently adjusting
	parameters of the composite model towards the in-domain data. On the standard
	tasks of translating English-to-German and Arabic-to-English TED talks, we
	observed average improvements of +0.9 and +0.7 BLEU points, respectively over a
	competition grade phrase-based system. We also demonstrate improvements over
	existing adaptation methods.},
  url       = {http://aclweb.org/anthology/C16-1299}
}