  Semantic representations have long been argued as potentially useful for
enforcing meaning preservation and improving generalization performance of
machine translation methods. In this work, we are the first to incorporate
information about predicate-argument structure of source sentences (namely,
semantic-role representations) into neural machine translation. We use Graph
Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders
and achieve improvements in BLEU scores over the linguistic-agnostic and
syntax-aware versions on the English--German language pair.
