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dc.contributor.author | Ruiz-Dolz, Ramon | es_ES |
dc.contributor.author | Alemany-Bordera, José | es_ES |
dc.contributor.author | Heras, Stella | es_ES |
dc.contributor.author | García-Fornes, A | es_ES |
dc.date.accessioned | 2022-07-25T18:06:29Z | |
dc.date.available | 2022-07-25T18:06:29Z | |
dc.date.issued | 2021-11-01 | es_ES |
dc.identifier.issn | 1541-1672 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/184749 | |
dc.description | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | es_ES |
dc.description.abstract | [EN] Argument mining is defined as the task of automatically identifying and extracting argumentative components (e.g., premises, claims, etc.) and detecting the existing relations among them (i.e., support, attack, rephrase, no relation). One of the main issues when approaching this problem is the lack of data, and the size of the publicly available corpora. In this work, we use the recently annotated US2016 debate corpus. US2016 is the largest existing argument annotated corpus, which allows exploring the benefits of the most recent advances in natural language processing in a complex domain like argument (relation) mining. We present an exhaustive analysis of the behavior of transformer-based models (i.e., BERT, XLNET, RoBERTa, DistilBERT, and ALBERT) when predicting argument relations. Finally, we evaluate the models in five different domains, with the objective of finding the less domain-dependent model. We obtain a macro F1-score of 0.70 with the US2016 evaluation corpus, and a macro F1-score of 0.61 with the Moral Maze cross-domain corpus. | es_ES |
dc.description.sponsorship | This work was supported in part by the Spanish Government project under Grant TIN2017-89,156-R, in part by the FPI under grant BES-2015-074,498, and in part by the Valencian Government project under Grant PROMETEO/2018/002. The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPUs used for this research. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers | es_ES |
dc.relation.ispartof | IEEE Intelligent Systems | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Task analysis | es_ES |
dc.subject | Ethics | es_ES |
dc.subject | Natural language processing | es_ES |
dc.subject | Data mining | es_ES |
dc.subject | Standards | es_ES |
dc.subject | Intelligent systems | es_ES |
dc.subject | Computational modeling | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1109/MIS.2021.3073993 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-89156-R/ES/AGENTES INTELIGENTES PARA ASESORAR EN PRIVACIDAD EN REDES SOCIALES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEO%2F2018%2F002//TECNOLOGIES PER ORGANITZACIONS HUMANES EMOCIONALS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113416RB-I00/ES/AGENTES INTELIGENTES AFECTIVOS PARA PERSUADIR COMPORTAMIENTOS CIVICOS EN ENTORNOS VIRTUALES / | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//BES-2015-074498/ES/BES-2015-074498/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació | es_ES |
dc.description.bibliographicCitation | Ruiz-Dolz, R.; Alemany-Bordera, J.; Heras, S.; García-Fornes, A. (2021). Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation. IEEE Intelligent Systems. 36(6):62-70. https://doi.org/10.1109/MIS.2021.3073993 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1109/MIS.2021.3073993 | es_ES |
dc.description.upvformatpinicio | 62 | es_ES |
dc.description.upvformatpfin | 70 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 36 | es_ES |
dc.description.issue | 6 | es_ES |
dc.relation.pasarela | S\437008 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |