  The encoder-decoder framework for neural machine translation (NMT) has been
shown effective in large data scenarios, but is much less effective for
low-resource languages. We present a transfer learning method that
significantly improves Bleu scores across a range of low-resource languages.
Our key idea is to first train a high-resource language pair (the parent
model), then transfer some of the learned parameters to the low-resource pair
(the child model) to initialize and constrain training. Using our transfer
learning method we improve baseline NMT models by an average of 5.6 Bleu on
four low-resource language pairs. Ensembling and unknown word replacement add
another 2 Bleu which brings the NMT performance on low-resource machine
translation close to a strong syntax based machine translation (SBMT) system,
exceeding its performance on one language pair. Additionally, using the
transfer learning model for re-scoring, we can improve the SBMT system by an
average of 1.3 Bleu, improving the state-of-the-art on low-resource machine
translation.
