Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies

Shuhei Kurita; Anders Søgaard

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arXiv · 1906.01239

Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies

Shuhei Kurita·Anders Søgaard

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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.

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BibTeX RIS

Shuhei Kurita, Anders Søgaard. 2019-06-04. Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies. https://arxiv.org/abs/1906.01239

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