Abstract
Neural attention models have achieved great success in different NLP tasks. However, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we describe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural attention model and our results are also competitive against state-of-the-art systems that do not use extra linguistic resources.- Anthology ID:
- E17-1035
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 366–375
- Language:
- URL:
- https://aclanthology.org/E17-1035
- DOI:
- Cite (ACL):
- Xiaochang Peng, Chuan Wang, Daniel Gildea, and Nianwen Xue. 2017. Addressing the Data Sparsity Issue in Neural AMR Parsing. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 366–375, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Addressing the Data Sparsity Issue in Neural AMR Parsing (Peng et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/E17-1035.pdf