Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. 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. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NMT’s use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google’s Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units (”wordpieces”) for both input and output. 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. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. On the WMT’14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google’s phrase-based production system.

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  1. Duarte, Victor; Duarte, Diogo; Fonseca, Julia; Montecinos, Alexis: Benchmarking machine-learning software and hardware for quantitative economics (2020)
  2. Jagtap, Ameya D.; Kharazmi, Ehsan; Karniadakis, George Em: Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems (2020)
  3. Kool, Wouter; van Hoof, Herke; Welling, Max: Ancestral Gumbel-top-(k) sampling for sampling without replacement (2020)
  4. Sirignano, Justin; Spiliopoulos, Konstantinos: Mean field analysis of neural networks: a law of large numbers (2020)
  5. Tang, Meng; Liu, Yimin; Durlofsky, Louis J.: A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems (2020)
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  7. Zhang, Jiajun; Zhou, Long; Zhao, Yang; Zong, Chengqing: Synchronous bidirectional inference for neural sequence generation (2020)
  8. Ghica, Dan R.; Alyahya, Khulood: Latent semantic analysis of game models using LSTM (2019)
  9. Liu, Minliang; Liang, Liang; Sun, Wei: Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach (2019)
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  11. Denny Britz, Anna Goldie, Minh-Thang Luong, Quoc Le: Massive Exploration of Neural Machine Translation Architectures (2017) arXiv
  12. Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, Matt Post: Sockeye: A Toolkit for Neural Machine Translation (2017) arXiv
  13. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean: Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation (2016) arXiv