Transition-based Semantic Dependency Parsing with Pointer Networks
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Transition-based Semantic Dependency Parsing with Pointer NetworksDate
2020-07Citation
Daniel Fernández-González and Carlos Gómez-Rodríguez. 2020. Transition-based Semantic Dependency Parsing with Pointer Networks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7035–7046, Online. Association for Computational Linguistics. doi: 10.18653/v1/2020.acl-main.629
Abstract
[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.
Keywords
Computational linguistics
Directed graphs
Graphic methods
Natural language processing systems
Semantic Web
Syntactics
Trees (mathematics)
Directed graphs
Graphic methods
Natural language processing systems
Semantic Web
Syntactics
Trees (mathematics)
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Atribución 3.0 España