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. The rest of the model, which includes an encoder, decoder and attention module, 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. 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