  This year, the Nara Institute of Science and Technology (NAIST)/Carnegie
Mellon University (CMU) submission to the Japanese-English translation track of
the 2016 Workshop on Asian Translation was based on attentional neural machine
translation (NMT) models. In addition to the standard NMT model, we make a
number of improvements, most notably the use of discrete translation lexicons
to improve probability estimates, and the use of minimum risk training to
optimize the MT system for BLEU score. As a result, our system achieved the
highest translation evaluation scores for the task.
