Transition-based Semantic Dependency Parsing with Pointer Networks [article]

Daniel Fernández-González, Carlos Gómez-Rodríguez
2020 arXiv   pre-print
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
more » ... 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.
arXiv:2005.13344v2 fatcat:ym73vvlclvcj3egz2amvfy722y