Abstract
In Semantic Dependency Parsing (SDP), semantic relations form directed acyclic graphs, rather than trees. We propose a new iterative predicate selection (IPS) algorithm for SDP. Our IPS algorithm combines the graph-based and transition-based parsing approaches in order to handle multiple semantic head words. We train the IPS model using a combination of multi-task learning and task-specific policy gradient training. Trained this way, IPS achieves a new state of the art on the SemEval 2015 Task 18 datasets. Furthermore, we observe that policy gradient training learns an easy-first strategy.- Anthology ID:
- P19-1232
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2420–2430
- Language:
- URL:
- https://aclanthology.org/P19-1232
- DOI:
- 10.18653/v1/P19-1232
- Cite (ACL):
- Shuhei Kurita and Anders Søgaard. 2019. Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2420–2430, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies (Kurita & Søgaard, ACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/P19-1232.pdf
- Code
- shuheikurita/semrl