@InProceedings{yamagishi-EtAl:2016:WAT2016,
  author    = {Yamagishi, Hayahide  and  Kanouchi, Shin  and  Sato, Takayuki  and  Komachi, Mamoru},
  title     = {Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation},
  booktitle = {Proceedings of the 3rd Workshop on Asian Translation (WAT2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {203--210},
  abstract  = {In machine translation, we must consider the difference in expression between
	languages. For example, the active/passive voice may change in Japanese-English
	translation. The same verb in Japanese may be translated into different voices
	at each translation because the voice of a generated sentence cannot be
	determined using only the information of the Japanese sentence. Machine
	translation systems should consider the information structure to improve the
	coherence of the output by using several topicalization techniques such as
	passivization.
	Therefore, this paper reports on our attempt to control the voice of the
	sentence generated by an encoder-decoder model. To control the voice of the
	generated sentence, we added the voice information of the target sentence to
	the source sentence during the training. We then generated sentences with a
	specified voice by appending the voice information to the source sentence. We
	observed experimentally whether the voice could be controlled. The results
	showed that, we could control the voice of the generated sentence with 85.0%
	accuracy on average. In the evaluation of Japanese-English translation, we
	obtained a 0.73-point improvement in BLEU score by using gold voice labels.},
  url       = {http://aclweb.org/anthology/W16-4620}
}