Neural Machine Translation with Supervised Attention
Table 5: BLEU comparison for low-resource translation task. CSTAR03 is the development set while IWSLT04 is the test set. ‘*’ denotes that SA-NMT is significantly better than both NMT1 and NMT2 with p<0.01.
['Systems', 'CSTAR03', 'IWSLT04']
Firstly, we can see that both standard neural machine translation systems NMT1 and NMT2 are much worse than Moses with a substantial gap. This result is not difficult to understand: neural network systems typically require sufficient data to boost their performance, and thus low resource translation tasks are very challenging for them. Secondly, the proposed SA-NMT gains much over NMT2 similar to the case in the large scale task, and the gap towards Moses is narrowed substantially.