@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.",
}

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.
Garc{\'\i}a-D{\'\i}az, Jos{\'e} Antonio Pan, Ronghao Alcar{\'a}z-M{\'a}rmol, Gema Mar{\'\i}n-P{\'e}rez, Mar{\'\i}a Jos{\'e} Valencia-Garc{\'\i}a, Rafael
UMUTeam at SemEval-2023 Task 11: Ensemble Learning applied to Binary Supervised Classifiers with disagreements