Abstract
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 datasets among previous state-of-the-art graph-based parsers.
Highlights
In dependency parsing, the syntactic structure of a sentence is represented by means of a labelled tree, where each word is forced to be attached exclusively to another that acts as its head
It is worth mentioning that we manage to significantly reduce the amount of transitions necessary for generating directed acyclic graph (DAG) in comparison to those proposed in the complex transition systems by Choi and McCallum (2013) and Titov et al (2009), used in the semantic dependency parsing (SDP) systems by Wang et al (2018) and Du et al (2015), respectively
Standard split as in previous work (Almeida and Martins, 2015; Du et al, 2015) results in 33,964 training sentences from Sections 00-19 of the Wall Street Journal corpus (Marcus et al, 1993), 1,692 development sentences from Section 20, 1,410 sentences from Section 21 as in-domain test set, and 1,849 sentences sampled from the Brown Corpus
Summary
The syntactic structure of a sentence is represented by means of a labelled tree, where each word is forced to be attached exclusively to another that acts as its head. Graph-based algorithms have drawn more attention since adapting them to this task is relatively straightforward. These globally optimized methods independently score arcs (or sets of them) and search for a high-scoring graph by combining these scores. From one of the first graph-based DAG parsers proposed by McDonald and Pereira (2006) to the current state-of-the-art models (Wang et al, 2019; He and Choi, 2019), different graph-based SDP approaches have been presented, providing accuracies above their main competitors: transitionbased DAG algorithms
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