  We empirically investigate learning from partial feedback in neural machine
translation (NMT), when partial feedback is collected by asking users to
highlight a correct chunk of a translation. We propose a simple and effective
way of utilizing such feedback in NMT training. We demonstrate how the common
machine translation problem of domain mismatch between training and deployment
can be reduced solely based on chunk-level user feedback. We conduct a series
of simulation experiments to test the effectiveness of the proposed method. Our
results show that chunk-level feedback outperforms sentence based feedback by
up to 2.61% BLEU absolute.
