Mostrar o rexistro simple do ítem
Gamifying Machine Teaching: Human-in-the-Loop Approach for Diphthong and Hiatus Identification in Spanish Language
dc.contributor.author | Mosqueira-Rey, E. | |
dc.contributor.author | Fernández-Castaño, Sergio | |
dc.contributor.author | Alonso Ríos, David | |
dc.contributor.author | Vázquez-Cano, Esteban | |
dc.contributor.author | López-Meneses, Eloy | |
dc.date.accessioned | 2024-07-02T11:45:16Z | |
dc.date.available | 2024-07-02T11:45:16Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | E. Mosqueira-Rey, S. Fernández-Castaño, D. Alonso-Ríos, E. Vázquez-Cano, and E. López-Meneses, "Gamifying Machine Teaching: Human-in-the-Loop Approach for Diphthong and Hiatus Identification in Spanish Language", Procedia Computer Science, vol. 225, pp. 3086-3096, 2023, doi: 10.1016/j.procs.2023.10.302 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/37635 | |
dc.description | Presented at 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023, Athens 6 - 8 September 2023 | |
dc.description.abstract | [Abstract]: Human-in-the-Loop Machine Learning (HITL-ML) is a set of techniques that attempt to actively involve experts into the learning loop of machine learning (ML) models. One of these techniques is Machine Teaching (MT) which tries to apply techniques that come from the world of didactics within machine learning (ML), such as sorting the dataset according to its difficulty and presenting the cases to the model in incremental levels of complexity. In this work we propose a new twist to MT: since its foundation is to bring didactic techniques to ML, why not use this technique as a didactic method itself? In this case we propose the creation of an ML model for the identification of diphthongs and hiatuses in the Spanish language. The first step is to develop a deep learning model to identify diphthongs and hiatuses using Curriculum Learning (CL) and a sorted dataset that identifies simple and complex cases. The accuracy of this model identifies the upper limit of efficiency that we can obtain by training the model. The next step is to reset the weights of the model but retain its architecture and offer the model to the students for its training. The idea is that students use MT techniques to make the model learn again, but the ultimate goal is that students learn by teaching in an informal and gamified learning environment. The results show how a HITL strategy can make a model learn iteratively to identify diphthongs and hiatuses and a workflow is proposed to include this technique in the classroom. | es_ES |
dc.description.sponsorship | This work has been supported by the Spanish Government (PID2019-107194GB-I00/AEI/10.13039/501100011033) the Xunta de Galicia (ED431C 2022/44), the Centro de Investigación “CITIC” (ED431G 2019/01) with European Union ERDF funds and the “Cátedra de Educación y Tecnologías Emergentes, Gamificación e IA (EduEmer)” of the Universidad Pablo de Olavide. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/44 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier B.V. | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107194GB-I00/ES/ANALISIS DE ESTRATEGIAS PARA INCORPORAR HUMANOS AL PROCESO DE APRENDIZAJE AUTOMATICO Y SU APLICACION A LA INVESTIGACION DEL CANCER PANCREATICO | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.procs.2023.10.302 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Curriculum Learning | es_ES |
dc.subject | Human-in-the-Loop Machine Learning | es_ES |
dc.subject | Machine Teaching | es_ES |
dc.subject | Orthography | es_ES |
dc.title | Gamifying Machine Teaching: Human-in-the-Loop Approach for Diphthong and Hiatus Identification in Spanish Language | es_ES |
dc.type | info:eu-repo/semantics/article | 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 | Procedia Computer Science | es_ES |
UDC.volume | 225 | es_ES |
UDC.startPage | 3086 | es_ES |
UDC.endPage | 3096 | es_ES |
dc.identifier.doi | 10.1016/j.procs.2023.10.302 | |
UDC.conferenceTitle | KES 2023 | es_ES |