@InProceedings{chen-EtAl:2017:EMNLP20173,
  author    = {Chen, Kehai  and  Wang, Rui  and  Utiyama, Masao  and  Liu, Lemao  and  Tamura, Akihiro  and  Sumita, Eiichiro  and  Zhao, Tiejun},
  title     = {Neural Machine Translation with Source Dependency Representation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2846--2852},
  abstract  = {Source dependency information has been successfully introduced into statistical
	machine translation. However, there are only a few preliminary attempts for
	Neural Machine Translation (NMT), such as concatenating representations of
	source word and its dependency label together. In this paper, we propose a
	novel NMT with source dependency representation to improve translation
	performance of NMT, especially long sentences. Empirical results on NIST
	Chinese-to-English translation task show that our method achieves 1.6 BLEU
	improvements on average over a strong NMT system.},
  url       = {https://www.aclweb.org/anthology/D17-1304}
}