Massively Multilingual Neural Machine Translation
Table 6: En→X test BLEU on the 103-language corpus
['[EMPTY]', 'Ar', 'Az', 'Be', 'De', 'He', 'It', 'Nl', 'Ro', 'Sk', 'Tr', 'Avg.']
Again, both of the massively multilingual models perform better than the baselines when averaged across the 10 evaluated language pairs, while handling up to 102 languages to-and-from English and 204 translation tasks simultaneously. In this case the results are similar to those we observed on the TED talks corpus, where the one-to-many model performs better than the many-to-many model. Again, this advantage may be due to the one-to-many model handling a smaller number of tasks while not being biased towards English in the target side like the many-to-many model.