  We focus on graph-to-sequence learning, which can be framed as transducing
graph structures to sequences for text generation. To capture structural
information associated with graphs, we investigate the problem of encoding
graphs using graph convolutional networks (GCNs). Unlike various existing
approaches where shallow architectures were used for capturing local structural
information only, we introduce a dense connection strategy, proposing a novel
Densely Connected Graph Convolutional Networks (DCGCNs). Such a deep
architecture is able to integrate both local and non-local features to learn a
better structural representation of a graph. Our model outperforms the
state-of-the-art neural models significantly on AMRto-text generation and
syntax-based neural machine translation.
