Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
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- OA-Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
Final published version, 3.4 MB, PDF document
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.
Original language | English |
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Title of host publication | Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics |
Publisher | Association for Computational Linguistics |
Publication date | 2019 |
Pages | 2420-2430 |
DOIs | |
Publication status | Published - 2019 |
Event | 57th Annual Meeting of the Association for Computational Linguistics - Florence, Italy Duration: 1 Jul 2019 → 1 Jul 2019 |
Conference
Conference | 57th Annual Meeting of the Association for Computational Linguistics |
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Land | Italy |
By | Florence, |
Periode | 01/07/2019 → 01/07/2019 |
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