The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index.
A Graph-to-Sequence Model for AMR-to-Text Generation
Open accessAbstract
The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph.The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure.Although it is able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences.We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics.On a standard benchmark, our model shows superior results to existing methods in the literature.
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Cites
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Cited by
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A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
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A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. (2020) · UTS ePRESS (University of Technology Sydney) · Cited by 3,174
Recent advances and applications of deep learning methods in materials science
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A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
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Metadata
- Authors
- Song, L., Zhang, Y., Wang, Z., & Gildea, D.
- Affiliations
- University of Rochester (US)
- Singapore University of Technology and Design (SG)
- IBM (United States) (US)
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 1616-1626
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2963374482
- Cited by
- 216
- References
- 36
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 27 · 2022: 23 · 2021: 64 · 2020: 44 · 2019: 34 · 2018: 8
- FWCI
- 15.31
- 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)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- University of Rochester
- Centre International de Recherche sur le Cancer
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- AMR-to-text generation, Abstract Meaning Representation, graph neural networks, LSTM, sequence-to-sequence models
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