Graph-to-Sequence Learning using Gated Graph Neural Networks

Daniel Beck, Gholamreza Haffari, Trevor Cohn


Abstract
Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on graph-to-sequence obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results shows that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.
Anthology ID:
P18-1026
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
273–283
Language:
URL:
https://www.aclweb.org/anthology/P18-1026
DOI:
10.18653/v1/P18-1026
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-1026.pdf
Poster:
 P18-1026.Poster.pdf