Transition-based Semantic Dependency Parsing with Pointer Networks
This work addresses semantic parsing for NLP researchers, offering an incremental improvement by adapting existing methods to a harder problem.
The paper tackles semantic dependency parsing by proposing a transition-based system using Pointer Networks and BERT embeddings, achieving state-of-the-art accuracy that matches the best fully-supervised results on SemEval 2015 Task 18 English datasets.
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.