@InProceedings{sekizawa-kajiwara-komachi:2017:WAT2017,
  author    = {Sekizawa, Yuuki  and  Kajiwara, Tomoyuki  and  Komachi, Mamoru},
  title     = {Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language},
  booktitle = {Proceedings of the 4th Workshop on Asian Translation (WAT2017)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {64--69},
  abstract  = {Neural machine translation (NMT) produces sentences that are more fluent than
	those produced by statistical machine translation (SMT). However, NMT has a
	very high computational cost because of the high dimensionality of the output
	layer. Generally, NMT restricts the size of vocabulary, which results in
	infrequent words being treated as out-of-vocabulary (OOV) and degrades the
	performance of the translation. In evaluation, we achieved a statistically
	significant BLEU score improvement of 0.55-0.77 over the baselines including
	the state-of-the-art method.},
  url       = {http://www.aclweb.org/anthology/W17-5703}
}