  We address the problem of simultaneous translation by modifying the Neural MT
decoder to operate with dynamically built encoder and attention. We propose a
tunable agent which decides the best segmentation strategy for a user-defined
BLEU loss and Average Proportion (AP) constraint. Our agent outperforms
previously proposed Wait-if-diff and Wait-if-worse agents (Cho and Esipova,
2016) on BLEU with a lower latency. Secondly we proposed data-driven changes to
Neural MT training to better match the incremental decoding framework.
