Trivial Transfer Learning for Low-Resource Neural Machine Translation
Table 3: Maximal score reached by ENET child for decreasing sizes of child training data, trained off an ENFI parent (all ENFI data are used and models are trained for 800k steps). The baselines use only the reduced ENET data.
['Child Training Sents', 'Transfer BLEU', 'Baseline BLEU']
It is a common knowledge, that gains from transfer learning are more pronounced for smaller childs. Our transfer learning (“start with a model for whatever parent pair”) may thus resolve the issue of applicability of NMT for low resource languages as pointed out by \newcitekoehn-knowles:2017:NMT.