Multi-Source Neural Machine Translation with Missing Data
Table 2: Results in BLEU for one-to-one and multi-source ({Es, Fr, Ar}-to-En) translation on UN6WAY data (parentheses are BLEU gains against the best one-to-one results). ∗ indicates the difference from mixture of NMT experts is statistically significant (p<0.01).
['Condition', 'One-to-one Es-En', 'One-to-one Fr-En', 'One-to-one Ar-En', 'Multi-encoder', 'Mix. NMT Experts']
The multi-source approaches achieved consistent improvements over the one-to-one NMTs in the all conditions, as demonstrated in previous multi-source NMT studies. Our main focus here is Pseudo-incomplete (0.8M), in which the multi-source results were slightly worse than those in Complete (0.8M) but better than those in Complete (0.2M). This suggests the additional use of incomplete corpora is beneficial in multi-source NMT compared to the use of only the complete parts of the corpus, even if just through the simple modification of replacing missing sentences with  .