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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D18-1198.pdf