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    <titleInfo>
        <title>Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning</title>
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    <name type="personal">
        <namePart type="given">Zhijiang</namePart>
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        <namePart type="family">Zhang</namePart>
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    <name type="personal">
        <namePart type="given">Zhiyang</namePart>
        <namePart type="family">Teng</namePart>
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    <name type="personal">
        <namePart type="given">Wei</namePart>
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    <abstract>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 Network (DCGCN). 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 AMR-to-text generation and syntax-based neural machine translation.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00269</identifier>
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        <date>2019</date>
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%0 Journal Article
%T Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning
%A Guo, Zhijiang
%A Zhang, Yan
%A Teng, Zhiyang
%A Lu, Wei
%J Transactions of the Association for Computational Linguistics
%D 2019
%V 7
%I MIT Press
%C Cambridge, MA
%F guo-etal-2019-densely
%X 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 Network (DCGCN). 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 AMR-to-text generation and syntax-based neural machine translation.
%R 10.1162/tacl_a_00269
%U https://aclanthology.org/Q19-1019
%U https://doi.org/10.1162/tacl_a_00269
%P 297-312

Markdown (Informal)

[Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning](https://aclanthology.org/Q19-1019) (Guo et al., TACL 2019)

• Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning (Guo et al., TACL 2019)

ACL

• Zhijiang Guo, Yan Zhang, Zhiyang Teng, and Wei Lu. 2019. Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning. Transactions of the Association for Computational Linguistics, 7:297–312.