Incorporating Structural Alignment Biases into an Attentional Neural Translation Model
Table 4: BLEU scores on the test sets for re-ranking. bold: Best performance, ♠: Significantly better than Attentional, ♣: Using ensemble of models.
['[BOLD] Lang. Pair', '[BOLD] Zh-En', '[BOLD] Ru-En', '[BOLD] Et-En', '[BOLD] Ro-En']
We compare re-ranking settings using the log probabilities produced by our model as additional features vs. using log probabilities from the vanilla attentional model and the encoder-decoder. The re-rankers based on our model are significantly better than the rest for Chinese and Estonian, and on par with the other for Russian and Romanian→English. In all cases our model has performance at least 1 BLEU point better than the baseline phrase-based system. It is worth noting that for Chinese-English, our re-ranker leads to an increase of almost 3 points in the BLEU score using an ensemble of neural models with different configurations.