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dc.contributor.authorAlonso-Alonso, Iago
dc.contributor.authorVilares, David
dc.contributor.authorGómez-Rodríguez, Carlos
dc.date.accessioned2024-05-23T07:36:05Z
dc.date.available2024-05-23T07:36:05Z
dc.date.issued2022-07
dc.identifier.citationIago Alonso-Alonso, David Vilares, and Carlos Gómez-Rodríguez. 2022. LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1389–1400, Seattle, United States. Association for Computational Linguistics.es_ES
dc.identifier.urihttp://hdl.handle.net/2183/36586
dc.descriptionHeld 14-15 July 2022, Onlinees_ES
dc.description.abstract[Absctract]: This paper addressed the problem of structured sentiment analysis using a bi-affine semantic dependency parser, large pre-trained language models, and publicly available translation models. For the monolingual setup, we considered: (i) training on a single treebank, and (ii) relaxing the setup by training on treebanks coming from different languages that can be adequately processed by cross-lingual language models. For the zero-shot setup and a given target treebank, we relied on: (i) a word-level translation of available treebanks in other languages to get noisy, unlikely-grammatical, but annotated data (we release as much of it as licenses allow), and (ii) merging those translated treebanks to obtain training data. In the post-evaluation phase, we also trained cross-lingual models that simply merged all the English treebanks and did not use word-level translations, and yet obtained better results. According to the official results, we ranked 8th and 9th in the monolingual and cross-lingual setups.es_ES
dc.description.sponsorshipThis work is supported by a 2020 Leonardo Grant for Researchers and Cultural Creators from the FBBVA, 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.description.sponsorshipXunta de Galicia; ED431C 2020/11es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_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.semeval-1.193.pdfes_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectStructured Sentiment Analysises_ES
dc.subjectCross-Lingual Language Modelses_ES
dc.subjectSemantic Dependency Parsinges_ES
dc.subjectZero-Shot Learninges_ES
dc.titleLyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency 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 16th International Workshop on Semantic Evaluation (SemEval-2022)es_ES
UDC.startPage1389es_ES
UDC.endPage1400es_ES
UDC.conferenceTitle16th International Workshop on Semantic Evaluation (SemEval-2022)es_ES


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