@InProceedings{chen-EtAl:2017:I17-1,
  author    = {Chen, Kehai  and  Wang, Rui  and  Utiyama, Masao  and  Sumita, Eiichiro  and  Zhao, Tiejun},
  title     = {Context-Aware Smoothing for Neural Machine Translation},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {11--20},
  abstract  = {In Neural Machine Translation (NMT), each word is represented as a
	low-dimension, real-value vector for encoding its syntax and semantic
	information. This means that even if the word is in a different sentence
	context, it is represented as the fixed vector to learn source representation.
	Moreover, a large number of Out-Of-Vocabulary (OOV) words, which have different
	syntax and semantic information, are represented as the same vector
	representation of "unk". To alleviate this problem, we propose a novel
	context-aware smoothing method to dynamically learn a sentence-specific vector
	for each word (including OOV words) depending on its local context words in a
	sentence.  The learned context-aware representation is integrated into the NMT
	to improve the translation performance. Empirical results on NIST
	Chinese-to-English translation task show that the proposed approach achieves
	1.78 BLEU improvements on average over a strong attentional NMT, and
	outperforms some existing systems.},
  url       = {http://www.aclweb.org/anthology/I17-1002}
}