@inproceedings{fernandez-gonzalez-gomez-rodriguez-2020-transition,
title = "Transition-based Semantic Dependency Parsing with Pointer Networks",
author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel and
G{\'o}mez-Rodr{\'\i}guez, Carlos",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.629",
doi = "10.18653/v1/2020.acl-main.629",
pages = "7035--7046",
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.",
}
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%0 Conference Proceedings
%T Transition-based Semantic Dependency Parsing with Pointer Networks
%A Fernández-González, Daniel
%A Gómez-Rodríguez, Carlos
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F fernandez-gonzalez-gomez-rodriguez-2020-transition
%X 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.
%R 10.18653/v1/2020.acl-main.629
%U https://aclanthology.org/2020.acl-main.629
%U https://doi.org/10.18653/v1/2020.acl-main.629
%P 7035-7046
Markdown (Informal)
[Transition-based Semantic Dependency Parsing with Pointer Networks](https://aclanthology.org/2020.acl-main.629) (Fernández-González & Gómez-Rodríguez, ACL 2020)
ACL