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dc.contributor.authorMuñoz-Ortiz, Alberto
dc.contributor.authorGómez-Rodríguez, Carlos
dc.contributor.authorVilares, David
dc.date.accessioned2024-05-27T12:35:00Z
dc.date.available2024-05-27T12:35:00Z
dc.date.issued2022-05
dc.identifier.citationAlberto Muñoz-Ortiz, Carlos Gómez-Rodríguez, and David Vilares. 2022. Cross-lingual Inflection as a Data Augmentation Method for Parsing. In Proceedings of the Third Workshop on Insights from Negative Results in NLP, pages 54–61, Dublin, Ireland. Association for Computational Linguistics.es_ES
dc.identifier.urihttp://hdl.handle.net/2183/36647
dc.descriptionHeld 26 May 2022, Dublin, Ireland.es_ES
dc.description.abstract[Absctract]: We propose a morphology-based method for low-resource (LR) dependency parsing. We train a morphological inflector for target LR languages, and apply it to related rich-resource (RR) treebanks to create cross-lingual (x-inflected) treebanks that resemble the target LR language. We use such inflected treebanks to train parsers in zero- (training on x-inflected treebanks) and few-shot (training on x-inflected and target language treebanks) setups. The results show that the method sometimes improves the baselines, but not consistently.es_ES
dc.description.sponsorshipThis work is supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the FBBVA,3 as well as by the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150). The work is also supported by ERDF/MICINN-AEI (SCANNER-UDC, PID2020-113230RB-C21), by Xunta de Galicia (ED431C 2020/11), and by Centro de Investigación de Galicia “CITIC” which is funded by Xunta de Galicia, Spain and the European Union (ERDF - Galicia 2014–2020 Program), by grant ED431G 2019/01.es_ES
dc.language.isoenges_ES
dc.publisherAssociation for Computational Linguisticses_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/714150es_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113230RB-C21/ES/MODELOS MULTITAREA DE ETIQUETADO SECUENCIAL PARA EL RECONOCIMIENTO DE ENTIDADES ENRIQUECIDO CON INFORMACIÓN LINGÜÍSTICA: SINTAXIS E INTEGRACIÓN MULTITAREA (SCANNER-UDC)es_ES
dc.relation.urihttps://aclanthology.org/2022.insights-1.7/es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectCross-lingual inflectiones_ES
dc.subjectMorphological Inflectiones_ES
dc.subjectData augmentationes_ES
dc.subjectDependency parsinges_ES
dc.subjectLow-resource languageses_ES
dc.subjectSyntactic data augmentationes_ES
dc.titleCross-lingual Inflection as a Data Augmentation Method for Parsinges_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProceedings of the Third Workshop on Insights from Negative Results in NLPes_ES
UDC.startPage54es_ES
UDC.endPage61es_ES
UDC.conferenceTitleThird Workshop on Insights from Negative Results in NLP (Insights 2022)es_ES


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