@InProceedings{imamura-sumita:2017:WAT2017,
  author    = {Imamura, Kenji  and  Sumita, Eiichiro},
  title     = {Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017},
  booktitle = {Proceedings of the 4th Workshop on Asian Translation (WAT2017)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {127--134},
  abstract  = {In this paper, we describe the NICT-2 neural machine translation system
	evaluated at WAT2017.  This system uses multiple models as an ensemble and
	combines models with opposite decoding directions by reranking (called
	bi-directional reranking).
	In our experimental results on small data sets, the translation quality
	improved when the number of models was increased to 32 in total and did not
	saturate.  In the experiments on large data sets, improvements of 1.59-3.32
	BLEU points were achieved when six-model ensembles were combined by the
	bi-directional reranking.},
  url       = {http://www.aclweb.org/anthology/W17-5711}
}