@InProceedings{oda-EtAl:2017:Long,
  author    = {Oda, Yusuke  and  Arthur, Philip  and  Neubig, Graham  and  Yoshino, Koichiro  and  Nakamura, Satoshi},
  title     = {Neural Machine Translation via Binary Code Prediction},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {850--860},
  abstract  = {In this paper, we propose a new method for calculating the output layer in
	neural machine translation systems. The method is based on predicting a
	binary code for each word and can reduce computation time/memory requirements
	of the output layer to be logarithmic in vocabulary size in the best case.
	In addition, we also introduce two advanced approaches to improve the
	robustness of the proposed model: using error-correcting codes and combining
	softmax and binary codes. Experiments on two English-Japanese bidirectional
	translation tasks show proposed models achieve BLEU scores that approach the
	softmax, while reducing memory usage to the order of less than 1/10 and
	improving decoding speed on CPUs by x5 to x10.},
  url       = {http://aclweb.org/anthology/P17-1079}
}