Abstract
We introduce an open-domain neural semantic parser which generates formal meaning representations in the style of Discourse Representation Theory (DRT; Kamp and Reyle 1993). We propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages: basic DRS structure prediction, condition prediction (i.e., predicates and relations), and referent prediction (i.e., variables). Experimental results on the Groningen Meaning Bank (GMB) show that our model outperforms competitive baselines by a wide margin.- Anthology ID:
- P18-1040
- 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:
- 429–439
- Language:
- URL:
- https://aclanthology.org/P18-1040
- DOI:
- 10.18653/v1/P18-1040
- Cite (ACL):
- Jiangming Liu, Shay B. Cohen, and Mirella Lapata. 2018. Discourse Representation Structure Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 429–439, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Discourse Representation Structure Parsing (Liu et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P18-1040.pdf
- Code
- EdinburghNLP/EncDecDRSparsing
- Data
- Groningen Meaning Bank