@InProceedings{marcheggiani-perezbeltrachini:2018:W18-65,
  author    = {Marcheggiani, Diego  and  Perez-Beltrachini, Laura},
  title     = {Deep Graph Convolutional Encoders for Structured Data to Text Generation},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {1--9},
  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},
  url       = {http://www.aclweb.org/anthology/W18-6501}
}