  Machine translation is shifting to an end-to-end approach based on deep
neural networks. The state of the art achieves impressive results for popular
language pairs such as English - French or English - Chinese. However for
English - Vietnamese the shortage of parallel corpora and expensive
hyper-parameter search present practical challenges to neural-based approaches.
This paper highlights our efforts on improving English-Vietnamese translations
in two directions: (1) Building the largest open Vietnamese - English corpus to
date, and (2) Extensive experiments with the latest neural models to achieve
the highest BLEU scores. Our experiments provide practical examples of
effectively employing different neural machine translation models with
low-resource language pairs.
