Corpus ID: 3603249

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

@article{Wu2016GooglesNM,
  title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
  author={Y. Wu and Mike Schuster and Z. Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey and M. Krikun and Yuan Cao and Q. Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and M. Johnson and X. Liu and L. Kaiser and S. Gouws and Y. Kato and Taku Kudo and H. Kazawa and K. Stevens and G. Kurian and Nishant Patil and W. Wang and C. Young and J. Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and G. S. Corrado and Macduff Hughes and J. Dean},
  journal={ArXiv},
  year={2016},
  volume={abs/1609.08144}
}

Y. Wu, Mike Schuster, +28 authors J. Dean

Published 2016

Computer Science

ArXiv

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. [...] Key Method This method provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system.Expand Abstract

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