@InProceedings{fadaee-bisazza-monz:2017:Short2,
  author    = {Fadaee, Marzieh  and  Bisazza, Arianna  and  Monz, Christof},
  title     = {Data Augmentation for Low-Resource Neural Machine Translation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {567--573},
  abstract  = {The quality of a Neural Machine Translation system depends substantially on the
	availability of sizable parallel corpora.
	For low-resource language pairs this is not the case, resulting in poor
	translation quality. 
	Inspired by work in computer vision, we propose a novel data augmentation
	approach that targets low-frequency words by generating new sentence pairs
	containing rare words in new, synthetically created contexts.
	Experimental results on simulated low-resource settings show that our method
	improves translation quality by up to 2.9 BLEU points over the baseline and up
	to 3.2 BLEU over back-translation.},
  url       = {http://aclweb.org/anthology/P17-2090}
}