  This paper describes the AMU-UEDIN submissions to the WMT 2016 shared task on
news translation. We explore methods of decode-time integration of
attention-based neural translation models with phrase-based statistical machine
translation. Efficient batch-algorithms for GPU-querying are proposed and
implemented. For English-Russian, our system stays behind the state-of-the-art
pure neural models in terms of BLEU. Among restricted systems, manual
evaluation places it in the first cluster tied with the pure neural model. For
the Russian-English task, our submission achieves the top BLEU result,
outperforming the best pure neural system by 1.1 BLEU points and our own
phrase-based baseline by 1.6 BLEU. After manual evaluation, this system is the
best restricted system in its own cluster. In follow-up experiments we improve
results by additional 0.8 BLEU.
