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.

本研究通过图卷积网络（GCN）对图进行编码，引入了一种新的密集连接策略，提出了一种新型的密集连通图卷积网络（DCGCN）来集成本地和非本地特征，以更好地学习图的结构表示，并在 AMR-to-text 生成和基于语法的神经机器翻译上明显优于现有的神经模型。