Multi-Task Semantic Dependency Parsing with Policy Gradient forLearning 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.