Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

2019-08-16 · TACL 2019 3 · Zhijiang Guo, Yan Zhang, Zhiyang Teng, Wei Lu

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.

📄 PDF Abstract BibTeX arXiv:1908.05957

Code (1)

Cartus/DCGCN 공식 구현 mxnet

Tasks

Graph-to-Sequence Machine Translation Text Generation Translation

Methods 이 논문이 사용한 방법론

Graph Convolutional Networks 설명 없음

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