Deep Graph Convolutional Encoders for Structured Data to Text Generation
TL
TL;DR
This paper proposes an alternative encoder based on graph convolutional networks that directly exploits the input structure and reports results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.
Abstract
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.
References54 items
1
Graph-to-Sequence Learning using Gated Graph Neural Networks
2
Relational inductive biases, deep learning, and graph networks
3
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
4
Bootstrapping Generators from Noisy Data
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