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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Get To The Point: Summarization with Pointer-Generator Networks
See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775
Statistical phrase-based translation
Koehn, P., Och, F. J., & Marcu, D. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073445.1073462 · Cited by 3,265
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Tai, K. S., Socher, R., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1150 · Cited by 3,081
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Gu, J., Lu, Z., Li, H., & Li, V. O. K. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1154 · Cited by 1,553
Loopy Belief Propagation for Approximate Inference: An Empirical StudyPreprint
Murphy, K. P., Weiss, Y., & Jordan, M. I. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.6725 · Cited by 1,446
(2013) · Cited by 1,117
Statistical Phrase-Based Translation
Koehn, P., Och, F. J., & Marcu, D. (2003) · doi:10.21236/ada461156 · Cited by 970
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Marcheggiani, D., & Titov, I. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1159 · Cited by 907
Online large-margin training of dependency parsers
McDonald, R., Crammer, K., & Pereira, F. M. B. (2005) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1219840.1219852 · Cited by 795
Modeling Coverage for Neural Machine Translation
Tu, Z., Lu, Z., Liu, Y., Liu, X., & Li, H. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1008 · Cited by 747
Gülçehre, Ç., Ahn, S., Nallapati, R. M., Zhou, B., & Bengio, Y. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1014 · Cited by 541
Cross-Sentence N -ary Relation Extraction with Graph LSTMs
Peng, N., Poon, H., Quirk, C., Toutanova, K., & Yih, W.-T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00049 · Cited by 506
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Konstas, I., Iyer, S. V., Yatskar, M., Choi, Y., & Zettlemoyer, L. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1014 · Cited by 255
Neural Headline Generation on Abstract Meaning Representation
Takase, S., Suzuki, J., Okazaki, N., Hirao, T., & Nagata, M. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1112 · Cited by 164
Leveraging Context Information for Natural Question Generation
Song, L., Wang, Z., Hamza, W., Zhang, Y., & Gildea, D. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2090 · Cited by 146
Self-citationCross-Sentence N-ary Relation Extraction with Graph LSTMsPreprint
Peng, N., Poon, H., Quirk, C., Toutanova, K., & Yih, W.-T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.03743 · Cited by 128
Semantics-Based Machine Translation with Hyperedge Replacement Grammars
Jones, B., Andreas, J., Bauer, D., Hermann, K. M., & Knight, K. K. (2012) · International Conference on Computational Linguistics · Cited by 117
Generation from Abstract Meaning Representation using Tree Transducers
Flanigan, J., Dyer, C., Smith, N. A., & Carbonell, J. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1087 · Cited by 97
Mitra, A., & Baral, C. R. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10354 · Cited by 86
AMR-to-text Generation with Synchronous Node Replacement Grammar
Song, L., Peng, X., Zhang, Y., Wang, Z., & Gildea, D. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2002 · Cited by 78
Self-citationGenerating English from Abstract Meaning Representations
Pourdamghani, N., Knight, K. K., & Hermjakob, U. (2016) · International Conference on Natural Language Generation · doi:10.18653/v1/w16-6603 · Cited by 66
CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss
Flanigan, J., Dyer, C., Smith, N. A., & Carbonell, J. (2016) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s16-1186 · Cited by 48
Improving Event Detection with Abstract Meaning Representation
Li, X., Nguyen, T. H., Cao, K., & Grishman, R. (2015) · doi:10.18653/v1/w15-4502 · Cited by 43
FORGe at SemEval-2017 Task 9: Deep sentence generation based on a sequence of graph transducers
Mille, S., Carlini, R., Burga, A., & Wanner, L. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2158 · Cited by 24
Grūzītis, N., Goško, D., & Bārzdiņš, G. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2159 · Cited by 14
Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.
Λάμπουρας, Γ., & Vlachos, A. (2017) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s17-2096 · Cited by 12
Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
Self-citation
Research works printed but not linked: 3
- Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Simaan. 2017. Graph convolutional encoders for syntax-aware neural ma- chine translation. In Conference on Empirical Meth- ods in Natural Language Processing (EMNLP-17), pages 1957-1967, Copenhagen, Denmark.
- Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2017. AMR-to-text gen- eration with synchronous node replacement gram- mar. In Proceedings of the 55th Annual Meet- ing of the Association for Computational Linguistics (ACL-17), pages 7-13, Vancouver, Canada.
- Aleš Tamchyna, Chris Quirk, and Michel Galley. 2015. A discriminative model for semantics- to-string translation. In Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015), pages 30-36, Beijing, China.
Cited by
Sorted by citations
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
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
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
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., & Yu, P. S. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3177455 · Cited by 493
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
