This paper presents the University of Cambridge submission to WMT16.
Motivated by the complementary nature of syntactical machine translation and
neural machine translation (NMT), we exploit the synergies of Hiero and NMT in
different combination schemes. Starting out with a simple neural lattice
rescoring approach, we show that the Hiero lattices are often too narrow for
NMT ensembles. Therefore, instead of a hard restriction of the NMT search space
to the lattice, we propose to loosely couple NMT and Hiero by composition with
a modified version of the edit distance transducer. The loose combination
outperforms lattice rescoring, especially when using multiple NMT systems in an
ensemble