Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns
Table 5: Evaluation on English→German generic test set for HAN trained with k = {-2,-1,+1,+2} but decoded with varying context. ♠: Statistically significantly better than HAN with no context (k = ∅).
['[BOLD] Model', '[BOLD] BLEU', '[BOLD] APT', '[BOLD] F1-score']
However, when no context is used, there is a statistically significant drop in BLEU, while APT and F1-scores are equivalent to that of the baseline. This suggests that the model does rely on the context to achieve the improvement in pronoun translation. Further, we find that the future context is just as beneficial as the past context in improving general translation performance.