Multilingual Neural Machine Translation with Knowledge Distillation
Table 14: BLEU scores of the individual and multilingual models on the 44 languages→English on the Ted talk dataset.
['Language', 'Ar', 'Bg', 'Cs', 'Da', 'De', 'El', 'Es', 'Et', 'Fa']
It can be seen that while multilingual baseline performs worse than the individual model, multilingual model based on our method nearly matches and even outperforms the individual model. Note that the multilingual model handles 44 languages in total, which means our method can reduce the model parameters size to 1/44 without loss of accuracy.