@InProceedings{chen-EtAl:2017:Long6,
  author    = {Chen, Huadong  and  Huang, Shujian  and  Chiang, David  and  Chen, Jiajun},
  title     = {Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder},
  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     = {1936--1945},
  abstract  = {Most neural machine translation (NMT) models are based on the sequential
	encoder-decoder framework, which makes no use of syntactic information. In this
	paper, we improve this model by explicitly incorporating source-side syntactic
	trees. More specifically, we propose (1) a bidirectional tree
	encoder which learns both sequential and tree structured representations; (2) a
	tree-coverage model that lets the attention depend on the source-side syntax.
	Experiments on Chinese-English translation demonstrate that our proposed models
	outperform the sequential attentional model as well as a stronger baseline with
	a bottom-up tree encoder and word coverage.},
  url       = {http://aclweb.org/anthology/P17-1177}
}