Deep Graph Convolutional Encoders for Structured Data to Text Generation

Diego Marcheggiani, Laura Perez-Beltrachini


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.
Anthology ID:
W18-6501
Volume:
Proceedings of the 11th International Conference on Natural Language Generation
Month:
November
Year:
2018
Address:
Tilburg University, The Netherlands
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–9
Language:
URL:
https://aclanthology.org/W18-6501
DOI:
10.18653/v1/W18-6501
Bibkey:
Cite (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.
Cite (Informal):
Deep Graph Convolutional Encoders for Structured Data to Text Generation (Marcheggiani & Perez-Beltrachini, 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6501.pdf
Code
 diegma/graph-2-text +  additional community code
Data
WebNLG