Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Table 7: BLEU scores for English↔{Belarusian, Russian, Ukrainian} models.
['[EMPTY]', 'Zero-Shot', 'From-Scratch', 'Incremental']
On the Russian↔{Belarusian, Ukrainian} directions, the “Zero-Shot” model already achieves relatively high BLEU scores for all directions except one, without any explicit parallel data. This could be because these languages are linguistically related. In the “From-Scratch” column, we see that training a new model from scratch improves the zero-shot translation directions further. However, this strategy has a slightly negative effect on the English↔X directions because our oversampling strategy will reduce the frequency of the data from these directions. In the final column, we see that incremental training with direct parallel data recovers most of the BLEU score difference between the first two columns on the zero-shot language pairs. In summary, our shared architecture models the zero-shot language pairs quite well and hence enables us to easily improve their quality with a small amount of additional parallel data.