Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

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→French and surpasses state-of-the-art 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. 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.

Source: https://arxiv.org/pdf/1611.04558v1.pdf

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