Papperoni

2016

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

Yonghui Wu, M. Schuster, Ziru Chen, Quoc V. Le, M. Norouzi, W. Macherey, M. Krikun, Yue Cao, Q. Gao, K. Macherey, J. Klingner, A. Shah, M. J. Johnson, Xiaodong Liu, Lukasz Kaiser, S. Gouws, Y. Kato, T. Kudo, H. Kazawa, K. Stevens, G. Kurian, N. Patil, Wenyi Wang, C. Young, J. Smith, J. Riesa, A. Rudnick, Oriol Vinyals, G. S. Corrado, M. Hughes, Jeffrey Dean

Open PDF Google Scholar

citations

Cite Score

AI summary

This paper introduces GNMT, Google's Neural Machine Translation system, using deep LSTM networks with residual connections and wordpieces to handle rare words. The model achieves competitive results on WMT'14 benchmarks and reduces translation errors by 60% compared to Google's phrase-based system, based on human evaluation.

Main Contributions

Introduces GNMT, Google's Neural Machine Translation system, addressing accuracy and speed challenges in NMT.
Employs a deep LSTM network with 8 encoder and 8 decoder layers, using residual connections and attention mechanisms.
Utilizes wordpieces for handling rare words, balancing flexibility and efficiency.
Achieves competitive results on WMT'14 English-to-French and English-to-German benchmarks.
Reduces translation errors by 60% compared to Google’s phrase-based production system, based on human evaluation.

Abstract

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 – sometimes prohibitively so in the case of very large data sets and large models. Several authors have also charged that NMT systems lack robustness, particularly when input sentences contain 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 residual connections as well as attention connections from the decoder network to the encoder. 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. To directly optimize the translation BLEU scores, we consider refining the models by using reinforcement learning, but we found that the improvement in the BLEU scores did not reflect in the human evaluation. 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.

Citation Graph

Loading graph...

References [44]

Sort:

Filter:

[1]Deep Residual Learning for Image Recognition

K. He, X. Zhang, S. Ren, Jian Sun - 2016

20 papers in library cite