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
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 273–283
- Language:
- URL:
- https://aclanthology.org/P18-1026
- DOI:
- 10.18653/v1/P18-1026
- Cite (ACL):
- Daniel Beck, Gholamreza Haffari, and Trevor Cohn. 2018. Graph-to-Sequence Learning using Gated Graph Neural Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 273–283, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Graph-to-Sequence Learning using Gated Graph Neural Networks (Beck et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/P18-1026.pdf
- Code
- beckdaniel/acl2018_graph2seq + additional community code