Handling Syntactic Divergence in Low-resource Machine Translation
Table 3: BLEU of our approach (Reorder) with different amount of parallel sentences of ja-en and ug-en translation. Baselines are supervised learning (sup), supervised learning with back translation (back) and data augmentation with translated original English sentences (No-Reorder).
['Model', '3k NMT', '3k SMT', '6k NMT', '6k SMT', '10k NMT', '10k SMT', '20k NMT', '20k SMT', '400k NMT', '400k SMT', 'ug NMT', 'ug SMT']
We present the full results in Tab. For SMT, reordering has much better performance than no-reorder, but still lags behind the supervised counterpart.