Deep Graph Convolutional Encoders for Structured Data to Text Generation
Diego Marcheggiani, Laura Perez-Beltrachini · 2018
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 graphto-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.1