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://www.aclweb.org/anthology/W18-6501
DOI:
10.18653/v1/W18-6501
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6501.pdf