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Sequence Labeling Parsing by Learning across Representations
dc.contributor.author | Strzyz, Michalina | |
dc.contributor.author | Vilares, David | |
dc.contributor.author | Gómez-Rodríguez, Carlos | |
dc.date.accessioned | 2024-05-28T11:47:54Z | |
dc.date.available | 2024-05-28T11:47:54Z | |
dc.date.issued | 2019-07 | |
dc.identifier.citation | Michalina Strzyz, David Vilares, and Carlos Gómez-Rodríguez. 2019. Sequence Labeling Parsing by Learning across Representations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5350–5357, Florence, Italy. Association for Computational Linguistics. | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/36671 | |
dc.description | The 57th Annual Meeting of the Association for Computational Linguistics (ACL) took place in Florence (Italy) at the 'Fortezza da Basso' from July 28th to August 2nd, 2019. | es_ES |
dc.description.abstract | [Absctract]: We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). First, we show that adding a parsing paradigm as an auxiliary loss consistently improves the performance on the other paradigm. Secondly, we explore an MTL sequence labeling model that parses both representations, at almost no cost in terms of performance and speed. The results across the board show that on average MTL models with auxiliary losses for constituency parsing outperform single-task ones by 1.05 F1 points, and for dependency parsing by 0.62 UAS points. | 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). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2017/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Association for Computational Linguistics | 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://aclanthology.org/P19-1531/ | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Sequence labeling | es_ES |
dc.subject | Multitask learning (MTL) | es_ES |
dc.subject | Constituency parsing | es_ES |
dc.subject | Dependency parsing | es_ES |
dc.title | Sequence Labeling Parsing by Learning across Representations | 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.journalTitle | Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics | es_ES |
UDC.startPage | 5350 | es_ES |
UDC.endPage | 5357 | es_ES |
UDC.conferenceTitle | 57th Annual Meeting of the Association for Computational Linguistics (ACL) | es_ES |
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