AMR Parsing as Sequence-to-Graph Transduction

Sheng Zhang, Xutai Ma, Kevin Duh, Benjamin Van Durme


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).
Anthology ID:
P19-1009
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
80–94
Language:
URL:
https://aclanthology.org/P19-1009
DOI:
10.18653/v1/P19-1009
Bibkey:
Cite (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.
Cite (Informal):
AMR Parsing as Sequence-to-Graph Transduction (Zhang et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1009.pdf
Video:
 https://aclanthology.org/P19-1009.mp4
Code
 sheng-z/stog
Data
LDC2017T10