@article{johnson-etal-2017-googles,
title = "{G}oogle{'}s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation",
author = "Johnson, Melvin and
Schuster, Mike and
Le, Quoc V. and
Krikun, Maxim and
Wu, Yonghui and
Chen, Zhifeng and
Thorat, Nikhil and
Vi{\'e}gas, Fernanda and
Wattenberg, Martin and
Corrado, Greg and
Hughes, Macduff and
Dean, Jeffrey",
editor = "Lee, Lillian and
Johnson, Mark and
Toutanova, Kristina",
journal = "Transactions of the Association for Computational Linguistics",
volume = "5",
year = "2017",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q17-1024",
doi = "10.1162/tacl_a_00065",
pages = "339--351",
abstract = "We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT systems using a single model. On the WMT{'}14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-theart results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT{'}14 and WMT{'}15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. Our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and also show some interesting examples when mixing languages.",
}
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<abstract>We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT systems using a single model. On the WMT’14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-theart results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT’14 and WMT’15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. Our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and also show some interesting examples when mixing languages.</abstract>
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%0 Journal Article
%T Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
%A Johnson, Melvin
%A Schuster, Mike
%A Le, Quoc V.
%A Krikun, Maxim
%A Wu, Yonghui
%A Chen, Zhifeng
%A Thorat, Nikhil
%A Viégas, Fernanda
%A Wattenberg, Martin
%A Corrado, Greg
%A Hughes, Macduff
%A Dean, Jeffrey
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F johnson-etal-2017-googles
%X We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT systems using a single model. On the WMT’14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-theart results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT’14 and WMT’15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. Our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and also show some interesting examples when mixing languages.
%R 10.1162/tacl_a_00065
%U https://aclanthology.org/Q17-1024
%U https://doi.org/10.1162/tacl_a_00065
%P 339-351
Markdown (Informal)
[Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://aclanthology.org/Q17-1024) (Johnson et al., TACL 2017)
ACL
- Melvin Johnson, Mike Schuster, Quoc V. Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2017. Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation. Transactions of the Association for Computational Linguistics, 5:339–351.