Abstract
We demonstrate that an SHRG-based parser can produce semantic graphs much more accurately than previously shown, by relating synchronous production rules to the syntacto-semantic composition process. Our parser achieves an accuracy of 90.35 for EDS (89.51 for DMRS) in terms of elementary dependency match, which is a 4.87 (5.45) point improvement over the best existing data-driven model, indicating, in our view, the importance of linguistically-informed derivation for data-driven semantic parsing. This accuracy is equivalent to that of English Resource Grammar guided models, suggesting that (recurrent) neural network models are able to effectively learn deep linguistic knowledge from annotations.- Anthology ID:
- P18-1038
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 408–418
- Language:
- URL:
- https://aclanthology.org/P18-1038
- DOI:
- 10.18653/v1/P18-1038
- Cite (ACL):
- Yufei Chen, Weiwei Sun, and Xiaojun Wan. 2018. Accurate SHRG-Based Semantic Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 408–418, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Accurate SHRG-Based Semantic Parsing (Chen et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/P18-1038.pdf
- Data
- Penn Treebank