@InProceedings{wu-EtAl:2017:Long2,
  author    = {Wu, Shuangzhi  and  Zhang, Dongdong  and  Yang, Nan  and  Li, Mu  and  Zhou, Ming},
  title     = {Sequence-to-Dependency Neural Machine Translation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {698--707},
  abstract  = {Nowadays a typical Neural Machine Translation (NMT) model generates
	translations from left to right as a linear sequence, during which latent
	syntactic structures of the target sentences are not explicitly concerned.
	Inspired by the success of using syntactic knowledge of target language for
	improving statistical machine translation,
	in this paper we propose a novel Sequence-to-Dependency Neural Machine
	Translation (SD-NMT) method, in which the target word sequence and its
	corresponding dependency structure are jointly constructed and modeled, and
	this structure is used as context to facilitate word generations. Experimental
	results show that the proposed method significantly outperforms
	state-of-the-art baselines on Chinese-English and Japanese-English translation
	tasks.},
  url       = {http://aclweb.org/anthology/P17-1065}
}