The Missing Ingredient in Zero-Shot Neural Machine Translation
Table 4: WMT14 en-de-fr Zero-shot results with baseline and aligned models compared against pivoting. Pivoting through English is performed using the baseline multilingual model.
['[BOLD] Multilingual System', '[BOLD] Zero Shot  [ITALIC] de→ [ITALIC] fr', '[BOLD] Zero Shot  [ITALIC] fr→ [ITALIC] de', '[BOLD] Supervised  [ITALIC] en→ [ITALIC] fr', '[BOLD] Supervised  [ITALIC] en→ [ITALIC] de', '[BOLD] Supervised  [ITALIC] fr→ [ITALIC] en', '[BOLD] Supervised  [ITALIC] de→ [ITALIC] en']
We compare the zero-shot performance of the multilingual models against the pivoting with the same multilingual model. Pivoting was able to achieve BLEU scores of 26.25 on de→fr and 20.18 on fr→de as evaluated on newstest2013. The alignment losses also effectively disentangle the representation of the source sentence from the target language ensuring prediction in the desired language.