@InProceedings{zhou-EtAl:2017:Short1,
  author    = {Zhou, Long  and  Hu, Wenpeng  and  Zhang, Jiajun  and  Zong, Chengqing},
  title     = {Neural System Combination for Machine Translation},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {378--384},
  abstract  = {Neural machine translation (NMT) becomes a new approach to machine translation
	and generates much more fluent results compared to statistical machine
	translation (SMT). However, SMT is usually better than NMT in translation
	adequacy. It is therefore a promising direction to combine the advantages of
	both NMT and SMT. In this paper, we propose a neural system combination
	framework leveraging multi-source NMT, which takes as input the outputs of NMT
	and SMT systems and produces the final translation. Extensive experiments on
	the Chinese-to-English translation task show that our model archives
	significant improvement by 5.3 BLEU points over the best single system output
	and 3.4 BLEU points over the state-of-the-art traditional system combination
	methods.},
  url       = {http://aclweb.org/anthology/P17-2060}
}