The Missing Ingredient in Zero-Shot Neural Machine Translation
Table 1: 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] System', '[BOLD] Zero Shot  [ITALIC] de→ [ITALIC] fr', '[BOLD] Zero Shot  [ITALIC] fr→ [ITALIC] de', '[BOLD] Pivot  [ITALIC] de→ [ITALIC] fr', '[BOLD] Pivot  [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 see that the multilingual model performs well on the directions for which it received supervision: en↔de,fr. The 1-2 BLEU point regression as compared the one-to-one models is expected given that the multilingual model is trained to perform multiple tasks while using the same capacity.