@inproceedings{garcia-diaz-etal-2023-umuteam-semeval,
title = "{UMUT}eam at {S}em{E}val-2023 Task 11: Ensemble Learning applied to Binary Supervised Classifiers with disagreements",
author = "Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio and
Pan, Ronghao and
Alcar{\'a}z-M{\'a}rmol, Gema and
Mar{\'\i}n-P{\'e}rez, Mar{\'\i}a Jos{\'e} and
Valencia-Garc{\'\i}a, Rafael",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.145",
doi = "10.18653/v1/2023.semeval-1.145",
pages = "1061--1066",
abstract = "This paper describes the participation of the UMUTeam in the Learning With Disagreements (Le-Wi-Di) shared task proposed at SemEval 2023, which objective is the development of supervised automatic classifiers that consider, during training, the agreements and disagreements among the annotators of the datasets. Specifically, this edition includes a multilingual dataset. Our proposal is grounded on the development of ensemble learning classifiers that combine the outputs of several Large Language Models. Our proposal ranked position 18 of a total of 30 participants. However, our proposal did not incorporate the information about the disagreements. In contrast, we compare the performance of building several classifiers for each dataset separately with a merged dataset.",
}
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<abstract>This paper describes the participation of the UMUTeam in the Learning With Disagreements (Le-Wi-Di) shared task proposed at SemEval 2023, which objective is the development of supervised automatic classifiers that consider, during training, the agreements and disagreements among the annotators of the datasets. Specifically, this edition includes a multilingual dataset. Our proposal is grounded on the development of ensemble learning classifiers that combine the outputs of several Large Language Models. Our proposal ranked position 18 of a total of 30 participants. However, our proposal did not incorporate the information about the disagreements. In contrast, we compare the performance of building several classifiers for each dataset separately with a merged dataset.</abstract>
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%0 Conference Proceedings
%T UMUTeam at SemEval-2023 Task 11: Ensemble Learning applied to Binary Supervised Classifiers with disagreements
%A García-Díaz, José Antonio
%A Pan, Ronghao
%A Alcaráz-Mármol, Gema
%A Marín-Pérez, María José
%A Valencia-García, Rafael
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F garcia-diaz-etal-2023-umuteam-semeval
%X This paper describes the participation of the UMUTeam in the Learning With Disagreements (Le-Wi-Di) shared task proposed at SemEval 2023, which objective is the development of supervised automatic classifiers that consider, during training, the agreements and disagreements among the annotators of the datasets. Specifically, this edition includes a multilingual dataset. Our proposal is grounded on the development of ensemble learning classifiers that combine the outputs of several Large Language Models. Our proposal ranked position 18 of a total of 30 participants. However, our proposal did not incorporate the information about the disagreements. In contrast, we compare the performance of building several classifiers for each dataset separately with a merged dataset.
%R 10.18653/v1/2023.semeval-1.145
%U https://aclanthology.org/2023.semeval-1.145
%U https://doi.org/10.18653/v1/2023.semeval-1.145
%P 1061-1066
Markdown (Informal)
[UMUTeam at SemEval-2023 Task 11: Ensemble Learning applied to Binary Supervised Classifiers with disagreements](https://aclanthology.org/2023.semeval-1.145) (García-Díaz et al., SemEval 2023)
ACL