Better Transition-Based AMR Parsing with a Refined Search Space

Zhijiang Guo, Wei Lu


Abstract
This paper introduces a simple yet effective transition-based system for Abstract Meaning Representation (AMR) parsing. We argue that a well-defined search space involved in a transition system is crucial for building an effective parser. We propose to conduct the search in a refined search space based on a new compact AMR graph and an improved oracle. Our end-to-end parser achieves the state-of-the-art performance on various datasets with minimal additional information.
Anthology ID:
D18-1198
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1712–1722
Language:
URL:
https://aclanthology.org/D18-1198
DOI:
10.18653/v1/D18-1198
Bibkey:
Cite (ACL):
Zhijiang Guo and Wei Lu. 2018. Better Transition-Based AMR Parsing with a Refined Search Space. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1712–1722, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Better Transition-Based AMR Parsing with a Refined Search Space (Guo & Lu, EMNLP 2018)
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PDF:
https://aclanthology.org/D18-1198.pdf
Attachment:
 D18-1198.Attachment.zip