Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
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Dokumenter
- OA-Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies
Forlagets udgivne version, 3,4 MB, PDF-dokument
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.
Originalsprog | Engelsk |
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Titel | Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics |
Forlag | Association for Computational Linguistics |
Publikationsdato | 2019 |
Sider | 2420-2430 |
DOI | |
Status | Udgivet - 2019 |
Begivenhed | 57th Annual Meeting of the Association for Computational Linguistics - Florence, Italien Varighed: 1 jul. 2019 → 1 jul. 2019 |
Konference
Konference | 57th Annual Meeting of the Association for Computational Linguistics |
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Land | Italien |
By | Florence, |
Periode | 01/07/2019 → 01/07/2019 |
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