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    <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.</abstract>
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%0 Conference Proceedings
%T Deep Graph Convolutional Encoders for Structured Data to Text Generation
%A Marcheggiani, Diego
%A Perez-Beltrachini, Laura
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F marcheggiani-perez-beltrachini-2018-deep
%X 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.
%R 10.18653/v1/W18-6501
%U https://aclanthology.org/W18-6501
%U https://doi.org/10.18653/v1/W18-6501
%P 1-9

Markdown (Informal)

[Deep Graph Convolutional Encoders for Structured Data to Text Generation](https://aclanthology.org/W18-6501) (Marcheggiani & Perez-Beltrachini, INLG 2018)

• Deep Graph Convolutional Encoders for Structured Data to Text Generation (Marcheggiani & Perez-Beltrachini, INLG 2018)

ACL

• Diego Marcheggiani and Laura Perez-Beltrachini. 2018. Deep Graph Convolutional Encoders for Structured Data to Text Generation. In Proceedings of the 11th International Conference on Natural Language Generation, pages 1–9, Tilburg University, The Netherlands. Association for Computational Linguistics.