A GRU-Gated Attention Model for Neural Machine Translation© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Table 4: SAER and AER scores of word alignments deduced by different neural systems. The lower the score, the better the alignment quality.
['[BOLD] System', '[BOLD] SAER', '[BOLD] AER']
To verify this point, we evaluated the quality of word alignments induced from different neural systems in terms of alignment error rate (AER) [Och:2003:SCV:778822.778824] and the soft version (SAER) of AER, following Tu et al. [DBLP:journals/corr/TuLLLL16]. We find that both GAtt and GAtt-Inv significantly outperform RNNSearch in terms of both AER and SAER. Specifically, GAtt obtains a gain of 7.91 SAER and 7.3 AER points over RNNSearch. As we obtain word alignments by connecting target words to source words with the highest alignment probabilities computed according to their attention weights, the consistent improvements of our model over RNNSearch on AER score indicate that our model indeed learns more accurate attentions.