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Transition-based Semantic Dependency Parsing with Pointer Networks
dc.contributor.author | Fernández-González, Daniel | |
dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.date.accessioned | 2024-01-24T08:34:42Z | |
dc.date.available | 2024-01-24T08:34:42Z | |
dc.date.issued | 2020-07 | |
dc.identifier.citation | 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 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/35100 | |
dc.description.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. | es_ES |
dc.description.sponsorship | This work has received funding from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), from the ANSWER-ASAP project (TIN2017-85160-C2-1-R) from MINECO, and from Xunta de Galicia (ED431B 2017/01, ED431G 2019/01). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2017/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Association for Computational Linguistics (ACL) | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/714150 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-85160-C2-1-R/ES/AVANCES EN NUEVOS SISTEMAS DE EXTRACCION DE RESPUESTAS CON ANALISIS SEMANTICO Y APRENDIZAJE PROFUNDO/ | es_ES |
dc.relation.uri | https://doi.org/10.18653/v1/2020.acl-main.629 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Computational linguistics | es_ES |
dc.subject | Directed graphs | es_ES |
dc.subject | Graphic methods | es_ES |
dc.subject | Natural language processing systems | es_ES |
dc.subject | Semantic Web | es_ES |
dc.subject | Syntactics | es_ES |
dc.subject | Trees (mathematics) | es_ES |
dc.title | Transition-based Semantic Dependency Parsing with Pointer Networks | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.startPage | 7035 | es_ES |
UDC.endPage | 7046 | es_ES |
dc.identifier.doi | 10.18653/v1/2020.acl-main.629 | |
UDC.conferenceTitle | 58th Annual Meeting of the Association for Computational Linguistics | es_ES |
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