  We present an approach to interactive-predictive neural machine translation
that attempts to reduce human effort from three directions: Firstly, instead of
requiring humans to select, correct, or delete segments, we employ the idea of
learning from human reinforcements in form of judgments on the quality of
partial translations. Secondly, human effort is further reduced by using the
entropy of word predictions as uncertainty criterion to trigger feedback
requests. Lastly, online updates of the model parameters after every
interaction allow the model to adapt quickly. We show in simulation experiments
that reward signals on partial translations significantly improve character
F-score and BLEU compared to feedback on full translations only, while human
effort can be reduced to an average number of $5$ feedback requests for every
input.
