@InProceedings{niu-denkowski-carpuat:2018:WNMT2018,
  author    = {Niu, Xing  and  Denkowski, Michael  and  Carpuat, Marine},
  title     = {Bi-Directional Neural Machine Translation with Synthetic Parallel Data},
  booktitle = {Proceedings of the 2nd Workshop on Neural Machine Translation and Generation},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {84--91},
  abstract  = {Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in these difficult cases. Our technique trains a single model for both directions of a language pair, allowing us to back-translate source or target monolingual data without requiring an auxiliary model. We then continue training on the augmented parallel data, enabling a cycle of improvement for a single model that can incorporate any source, target, or parallel data to improve both translation directions. As a byproduct, these models can reduce training and deployment costs significantly compared to uni-directional models. Extensive experiments show that our technique outperforms standard back-translation in low-resource scenarios, improves quality on cross-domain tasks, and effectively reduces costs across the board.},
  url       = {http://www.aclweb.org/anthology/W18-2710}
}