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Better, Faster, Stronger Sequence Tagging Constituent Parsers
dc.contributor.author | Vilares, David | |
dc.contributor.author | Abdou, Mostafa | |
dc.contributor.author | Søgaard, Anders | |
dc.date.accessioned | 2024-05-27T07:48:24Z | |
dc.date.available | 2024-05-27T07:48:24Z | |
dc.date.issued | 2019-06 | |
dc.identifier.citation | David Vilares, Mostafa Abdou, and Anders Søgaard. 2019. Better, Faster, Stronger Sequence Tagging Constituent Parsers. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3372–3383, Minneapolis, Minnesota. Association for Computational Linguistics. | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/36638 | |
dc.description | NAACL-HLT 2019 was held at the Hyatt Regency in Minneapolis from June 2nd through 7th, 2019. | es_ES |
dc.description.abstract | [Absctract]: Sequence tagging models for constituent parsing are faster, but less accurate than other types of parsers. In this work, we address the following weaknesses of such constituent parsers: (a) high error rates around closing brackets of long constituents, (b) large label sets, leading to sparsity, and (c) error propagation arising from greedy decoding. To effectively close brackets, we train a model that learns to switch between tagging schemes. To reduce sparsity, we decompose the label set and use multi-task learning to jointly learn to predict sublabels. Finally, we mitigate issues from greedy decoding through auxiliary losses and sentence-level fine-tuning with policy gradient. Combining these techniques, we clearly surpass the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebanks, and reduce their parsing time even further. On the SPMRL datasets, we observe even greater improvements across the board, including a new state of the art on Basque, Hebrew, Polish and Swedish. | es_ES |
dc.description.sponsorship | DV has received support from the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150), from the TELEPARES-UDC project (FFI2014-51978-C2-2-R) and the ANSWERASAP project (TIN2017-85160-C2-1-R) from MINECO, and from Xunta de Galicia (ED431B 2017/01). MA and AS are funded by a Google Focused Research Award. | 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/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FFI2014-51978-C2-2-R/ES/TECNOLOGIAS DE LA LENGUA PARA ANALISIS DE OPINIONES EN REDES SOCIALES: DEL TEXTO AL MICROTEXTO | 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/N19-1341/ | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Sequence Tagging | es_ES |
dc.subject | Constituent Parsing | es_ES |
dc.subject | Multi-Task Learning | es_ES |
dc.subject | Parsing Error Mitigation | es_ES |
dc.title | Better, Faster, Stronger Sequence Tagging Constituent Parsers | 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 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) | es_ES |
UDC.startPage | 3372 | es_ES |
UDC.endPage | 3383 | es_ES |
UDC.conferenceTitle | 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) | es_ES |
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