The DOI matches its Crossref record; 45 of the 47 references it cites were found in a registry or an index.
Graph-to-Sequence Learning using Gated Graph Neural Networks
Open accessAbstract
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.
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Metadata
- Authors
- Beck, D., Haffari, G., & Cohn, T.
- Affiliations
- The University of Melbourne (AU)
- Monash University (AU)
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 273-283
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2798749466
- Cited by
- 380
- References
- 47
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 20 · 2023: 47 · 2022: 62 · 2021: 102 · 2020: 67 · 2019: 56 · 2018: 3
- FWCI
- 23.27
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- Johns Hopkins University
- Carnegie Mellon University
- Australian Research Council (DP160102686)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- gated graph neural networks, graph neural networks, semantic parsing
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