  Sequence-to-sequence learning involves a trade-off between signal strength
and annotation cost of training data. For example, machine translation data
range from costly expert-generated translations that enable supervised
learning, to weak quality-judgment feedback that facilitate reinforcement
learning. We present the first user study on annotation cost and machine
learnability for the less popular annotation mode of error markings. We show
that error markings for translations of TED talks from English to German allow
precise credit assignment while requiring significantly less human effort than
correcting/post-editing, and that error-marked data can be used successfully to
fine-tune neural machine translation models.
