Deep Graph Convolutional Encoders for Structured Data to Text Generation

Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative encoder based on graph convolutional networks that directly exploits the input structure. We report results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Data-to-Text Generation SR11Deep GCN + feat BLEU 0.666 # 2
Data-to-Text Generation WebNLG GCN EC BLEU 55.9 # 12