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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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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
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
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
(2014) · Cited by 33,419
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
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
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570
Spectral Networks and Locally Connected Networks on Graphs
Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6203 · Cited by 3,983
Gated Graph Sequence Neural NetworksPreprint
Li, Y., Tarlow, D., Brockschmidt, M., & Zemel, R. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05493 · Cited by 3,329
Convolutional Sequence to Sequence Learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850 · cited 1×
“Inspired by Gehring et al. (2017) and Vaswani et al. (2017), we tackle this problem by adding positional embeddings to every node.”
MethodConvolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628
A new model for learning in graph domains
Gori, M., Monfardini, G., & Scarselli, F. (2006) · Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. · doi:10.1109/ijcnn.2005.1555942 · Cited by 2,075
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., & Zhang, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.01274 · Cited by 1,819
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631
Moses : open source toolkit for statistical machine translation
Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., et al. (2007) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 1,547
(2013) · Cited by 1,117
Discriminative training and maximum entropy models for statistical machine translation
Och, F. J., & Ney, H. (2001) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1073083.1073133 · Cited by 1,035
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
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Wu, D. (1997) · Cited by 848
A syntax-based statistical translation model
Yamada, K., & Knight, K. K. (2001) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1073012.1073079 · Cited by 728
Reimers, N., & Gurevych, I. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1035 · Cited by 463
Galley, M., Hopkins, M., Knight, K. K., & Marcu, D. (2004) · doi:10.21236/ada460212 · Cited by 460
chrF++: words helping character n-grams
Popović, M. (2017) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w17-4770 · Cited by 286
Tree-to-Sequence Attentional Neural Machine Translation
Eriguchi, A., Hashimoto, K., & Tsuruoka, Y. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1078 · Cited by 279
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
Sockeye: A Toolkit for Neural Machine TranslationPreprint
Hieber, F., Domhan, T., Denkowski, M., Vilar, D., Sokolov, A., Clifton, A., & Post, M. (2017) · arXiv.org · doi:10.48550/arxiv.1712.05690 · Cited by 218
Modeling Relational Data with Graph Convolutional NetworksPreprint
Schlichtkrull, M., Kipf, T., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06103 · Cited by 181
Towards String-To-Tree Neural Machine Translation
Aharoni, R., & Goldberg, Y. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2021 · Cited by 159
Learning to Parse and Translate Improves Neural Machine Translation
Eriguchi, A., Tsuruoka, Y., & Cho, K. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2012 · Cited by 152
Results of the WMT16 Metrics Shared Task
Bojar, O., Graham, Y., Kamran, A., & Stanojević, M. (2016) · Conference on Machine Translation · doi:10.18653/v1/w16-2302 · Cited by 128
Results of the WMT17 Metrics Shared Task
Bojar, O., Graham, Y., & Kamran, A. (2017) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w17-4755 · Cited by 120
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
A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing
Peng, X., Song, L., & Gildea, D. (2015) · doi:10.18653/v1/k15-1004 · Cited by 90
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
Parsing Graphs with Hyperedge Replacement Grammars
Chiang, D., Andreas, J., Bauer, D., Hermann, K. M., Jones, B., & Knight, K. K. (2013) · Cited by 75
Generating 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
Neural Machine Translation with Source-Side Latent Graph Parsing
Hashimoto, K., & Tsuruoka, Y. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1012 · Cited by 54
Randomized Significance Tests in Machine Translation
Graham, Y., Mathur, N., & Baldwin, T. (2014) · Conference on Machine Translation (WMT) · doi:10.3115/v1/w14-3333 · Cited by 51
Levi, F. W. (1942) · Calcutta University Press eBooks · Cited by 44
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
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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
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Wu, S., Sun, F., Zhang, W., Xie, X. H., & Cui, B. (2022) · ACM Computing Surveys · doi:10.1145/3535101 · Cited by 1,571
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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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