@inproceedings{marcheggiani-perez-beltrachini-2018-deep,
    title = "Deep Graph Convolutional Encoders for Structured Data to Text Generation",
    author = "Marcheggiani, Diego  and
      Perez-Beltrachini, Laura",
    editor = "Krahmer, Emiel  and
      Gatt, Albert  and
      Goudbeek, Martijn",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-6501/",
    doi = "10.18653/v1/W18-6501",
    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 graph structure."
}