AMR-to-text Generation with Synchronous Node Replacement Grammar

Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, Daniel Gildea


Abstract
This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on a standard benchmark, our method gives the state-of-the-art result.
Anthology ID:
P17-2002
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–13
Language:
URL:
https://aclanthology.org/P17-2002
DOI:
10.18653/v1/P17-2002
Bibkey:
Cite (ACL):
Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2017. AMR-to-text Generation with Synchronous Node Replacement Grammar. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 7–13, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
AMR-to-text Generation with Synchronous Node Replacement Grammar (Song et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-2002.pdf
Video:
 https://aclanthology.org/P17-2002.mp4