Neural Machine Translation with Supervised Attention
Table 1: Performance of SA-NMT on development set for different loss functions to supervise the attention in terms of BLEU.
['Alignment Losses', 'BLEU']
We implemented three different losses to supervise the attention as described in §3.2. To explore their behaviors on the development set, we employed the GIZA++ to generate the alignment on the training set prior to the training SA-NMT. Furthermore, CE performs best among all losses, and thus we adopt it for the following experiments.