@inproceedings{zhang-etal-2019-amr,
title = "{AMR} Parsing as Sequence-to-Graph Transduction",
author = "Zhang, Sheng and
Ma, Xutai and
Duh, Kevin and
Van Durme, Benjamin",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1009",
doi = "10.18653/v1/P19-1009",
pages = "80--94",
abstract = "We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3{\%} on LDC2017T10) and AMR 1.0 (70.2{\%} on LDC2014T12).",
}
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%0 Conference Proceedings
%T AMR Parsing as Sequence-to-Graph Transduction
%A Zhang, Sheng
%A Ma, Xutai
%A Duh, Kevin
%A Van Durme, Benjamin
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhang-etal-2019-amr
%X We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3% on LDC2017T10) and AMR 1.0 (70.2% on LDC2014T12).
%R 10.18653/v1/P19-1009
%U https://aclanthology.org/P19-1009
%U https://doi.org/10.18653/v1/P19-1009
%P 80-94
Markdown (Informal)
[AMR Parsing as Sequence-to-Graph Transduction](https://aclanthology.org/P19-1009) (Zhang et al., ACL 2019)
ACL
- Sheng Zhang, Xutai Ma, Kevin Duh, and Benjamin Van Durme. 2019. AMR Parsing as Sequence-to-Graph Transduction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 80–94, Florence, Italy. Association for Computational Linguistics.