Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Table 2: One to Many: BLEU scores on various data sets for single language pair and multilingual models.
['Model', 'Single', 'Multi', 'Diff']
We see that the multilingual models are comparable to, and in some cases outperform, the baselines, but not always. We obtain a large gain of +0.9 BLEU for English→Spanish. Unlike the previous set of results, there are less significant gains in this set of experiments. This is perhaps due to the fact that the decoder has a more difficult time translating into multiple target languages which may even have different scripts, which are combined into a single shared wordpiece vocabulary. Note that even for languages with entirely different scripts (e.g. Korean and Japanese) there is significant overlap in wordpieces when real data is used, as often numbers, dates, names, websites, punctuation etc. are actually using a shared script (ASCII).