We propose a simple, elegant solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architecture from our base system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. The rest of the model, which includes encoder, decoder and attention, remains unchanged and is shared across all languages. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT using a single model without any increase in parameters, which is significantly simpler than previous proposals for Multilingual NMT. Our method often improves the translation quality of all involved language pairs, even while keeping the total number of model parameters constant. On the WMT'14 benchmarks, a single multilingual model achieves comparable performance for English$\rightarrow$French and surpasses state-of-the-art results for English$\rightarrow$German. Similarly, a single multilingual model surpasses state-of-the-art results for French$\rightarrow$English and German$\rightarrow$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. In addition to improving the translation quality of language pairs that the model was trained with, 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 show some interesting examples when mixing languages.

该研究提出一种简单的解决方案，使用单个神经机器翻译模型在多种语言之间进行翻译，并且通过在输入句子的开头引入人工标记来指定所需的目标语言，这种方法不需要更改模型框架，该模型的剩余组件包括编码器、解码器和注意力是不变的，并共享所有语言。我们的方法使用共享的词块词汇表，不需要增加任何参数，在保持模型参数总数恒定的情况下，还经常提高所有涉及的语言对的翻译质量，甚至可以在训练期间从未看到的语言对之间进行隐式桥接，因此，我们的翻译模型不限于训练时的语言对，具有一定的通用性和迁移能力。

Google的多语言神经机器翻译系统：实现零样本翻译