@InProceedings{wang-EtAl:2017:EMNLP20179,
  author    = {Wang, Longyue  and  Tu, Zhaopeng  and  Way, Andy  and  Liu, Qun},
  title     = {Exploiting Cross-Sentence Context for Neural Machine Translation},
  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     = {2826--2831},
  abstract  = {In translation, considering the document as a whole can help to resolve
	ambiguities and inconsistencies. In this paper, we propose a cross-sentence
	context-aware approach and investigate the influence of historical contextual
	information on the performance of neural machine translation (NMT). First, this
	history is summarized in a hierarchical way. We then integrate the historical
	representation into NMT in two strategies: 1) a warm-start of encoder and
	decoder states, and 2) an auxiliary context source for updating decoder states.
	Experimental results on a large Chinese-English translation task show that our
	approach significantly improves upon a strong attention-based NMT system by up
	to +2.1 BLEU points.},
  url       = {https://www.aclweb.org/anthology/D17-1301}
}