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

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.