  Transition-based parsers implemented with Pointer Networks have become the
new state of the art in dependency parsing, excelling in producing labelled
syntactic trees and outperforming graph-based models in this task. In order to
further test the capabilities of these powerful neural networks on a harder NLP
problem, we propose a transition system that, thanks to Pointer Networks, can
straightforwardly produce labelled directed acyclic graphs and perform semantic
dependency parsing. In addition, we enhance our approach with deep
contextualized word embeddings extracted from BERT. The resulting system not
only outperforms all existing transition-based models, but also matches the
best fully-supervised accuracy to date on the SemEval 2015 Task 18 English
datasets among previous state-of-the-art graph-based parsers.
