Neural AMR: Sequence-to-Sequence Models for Parsing and Generation

Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, Luke Zettlemoyer


Abstract
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 non-sequential 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 sequence-based AMR models are robust against ordering variations of graph-to-sequence conversions.
Anthology ID:
P17-1014
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
146–157
Language:
URL:
https://aclanthology.org/P17-1014
DOI:
10.18653/v1/P17-1014
Bibkey:
Cite (ACL):
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. 2017. Neural AMR: Sequence-to-Sequence Models for Parsing and Generation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 146–157, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation (Konstas et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1014.pdf
Presentation:
 P17-1014.Presentation.pdf
Video:
 https://vimeo.com/234952236
Code
 additional community code