The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Open accessAbstract
Sequence-to-sequence models have shown strong performance across a broad range of applications.However, their application to parsing and generating text using Abstract Meaning Representation (AMR) has been limited, due to the relatively limited amount of labeled data and the nonsequential nature of the AMR graphs.We present a novel training procedure that can lift this limitation using millions of unlabeled sentences and careful preprocessing of the AMR graphs.For AMR parsing, our model achieves competitive results of 62.1 SMATCH, the current best score reported without significant use of external semantic resources.For AMR generation, our model establishes a new state-of-the-art performance of BLEU 33.8.We present extensive ablative and qualitative analysis including strong evidence that sequencebased AMR models are robust against ordering variations of graph-to-sequence conversions.
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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
Moses: open source toolkit for statistical machine translation
Koehn, P., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., et al. (2007) · Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions - ACL '07 · doi:10.3115/1557769.1557821 · Cited by 4,735
Cited by
Neural Motifs: Scene Graph Parsing with Global Context
Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00611 · Cited by 1,052
Self-citationGraph-to-Sequence Learning using Gated Graph Neural Networks
Beck, D., Haffari, G., & Cohn, T. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1026 · Cited by 380
A Graph-to-Sequence Model for AMR-to-Text Generation
Song, L., Zhang, Y., Wang, Z., & Gildea, D. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1150 · Cited by 216
A Survey of Evaluation Metrics Used for NLG Systems
Sai, A. B., Mohankumar, A. K., & Khapra, M. M. (2023) · ACM Computing Surveys · doi:10.1145/3485766 · Cited by 235
Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue
Balakrishnan, A., Rao, J., Upasani, K., White, M., & Subba, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1080 · Cited by 70
Metadata
- Authors
- Konstas, I., Iyer, S., Yatskar, M., Choi, Y., & Zettlemoyer, L.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 146-157
- OpenAlex
- W2610308073
- Cited by
- 252
- References
- 2
- FWCI
- 20.20
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Konstas, I., Iyer, S., Yatskar, M., Choi, Y., & Zettlemoyer, L. (2017). Neural AMR: Sequence-to-Sequence Models for Parsing and Generation. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p17-1014
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