@InProceedings{zhang-EtAl:2016:COLING3,
  author    = {Zhang, Jian  and  Li, Liangyou  and  Way, Andy  and  Liu, Qun},
  title     = {Topic-Informed Neural Machine Translation},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1807--1817},
  abstract  = {In recent years, neural machine translation (NMT) has demonstrated
	state-of-the-art machine translation (MT) performance. It is a new approach to
	MT, which tries to learn a set of parameters to maximize the conditional
	probability of target sentences given source sentences. In this paper, we
	present a novel approach to improve the translation performance in NMT by
	conveying topic knowledge during translation. The proposed topic-informed NMT
	can increase the likelihood of
	selecting words from the same topic and domain for translation. Experimentally,
	we demonstrate that topic-informed NMT can achieve a 1.15 (3.3% relative) and
	1.67 (5.4% relative) absolute improvement in BLEU score on the
	Chinese-to-English language pair using NIST 2004 and 2005 test sets,
	respectively, compared to NMT without topic information.},
  url       = {http://aclweb.org/anthology/C16-1170}
}