  We describe the University of Maryland machine translation systems submitted
to the WMT17 German-English Bandit Learning Task. The task is to adapt a
translation system to a new domain, using only bandit feedback: the system
receives a German sentence to translate, produces an English sentence, and only
gets a scalar score as feedback. Targeting these two challenges (adaptation and
bandit learning), we built a standard neural machine translation system and
extended it in two ways: (1) robust reinforcement learning techniques to learn
effectively from the bandit feedback, and (2) domain adaptation using data
selection from a large corpus of parallel data.
