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

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 AMR-to-text generation and syntax-based neural machine translation.


  • There are currently no refbacks.

Copyright (c) 2019 Association for Computational Linguistics

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.