  We present a simple and effective approach to incorporating syntactic
structure into neural attention-based encoder-decoder models for machine
translation. We rely on graph-convolutional networks (GCNs), a recent class of
neural networks developed for modeling graph-structured data. Our GCNs use
predicted syntactic dependency trees of source sentences to produce
representations of words (i.e. hidden states of the encoder) that are sensitive
to their syntactic neighborhoods. GCNs take word representations as input and
produce word representations as output, so they can easily be incorporated as
layers into standard encoders (e.g., on top of bidirectional RNNs or
convolutional neural networks). We evaluate their effectiveness with
English-German and English-Czech translation experiments for different types of
encoders and observe substantial improvements over their syntax-agnostic
versions in all the considered setups.
