Neural Machine Translation with Key-Value Memory-Augmented Attention
Table 2: Performance on manually aligned Chinese-English test set. Higher BLEU score and lower AER score indicate better quality.
['Architecture', 'BLEU', '△', 'AER', '▽']
S4SS2SSS0Px3 Alignment Quality Intuitively, our KVMemAtt can enhance the attention and therefore improve the word alignment quality. We use the alignment error rate (AER) As expected, our KVMemAtt achieves better BLEU and AER scores (the lower the AER score, the better the alignment quality) than the strong baseline systems. Additionally, the results also indicate that the EOS-attention objective can assist the learning of attention-based NMT, since adding this objective yields better alignment performance.