Massively Multilingual Neural Machine Translation
Table 3: En→X test BLEU on the TED Talks corpus
['[EMPTY]', 'En-Az', 'En-Be', 'En-Gl', 'En-Sk', 'Avg.']
In this case we see an opposite trend: the many-to-many model performs worse than the one-to-many model by 2.53 BLEU on average. While previous works Wang et al. , this shows that increasing the number of source languages also causes additional degradation in a many-to-many model. This degradation may be due to the English-centric setting: since most of the translation directions the model is trained on are into English, this leaves less capacity for the other target languages (while still performing better than the bilingual baselines on all 8 language pairs). We also note that in this case the results are consistent among the higher and lower resource pairs – the one-to-many model is better than the many-to-many model, which outperforms the bilingual baselines in all cases. This is unlike the difference we saw in the X→En experiments since here we do not have the multi-way-parallel overfitting issue.