  Neural encoder-decoder models of machine translation have achieved impressive
results, while learning linguistic knowledge of both the source and target
languages in an implicit end-to-end manner. We propose a framework in which our
model begins learning syntax and translation interleaved, gradually putting
more focus on translation. Using this approach, we achieve considerable
improvements in terms of BLEU score on relatively large parallel corpus (WMT14
English to German) and a low-resource (WIT German to English) setup.
