RT Conference Proceedings
T1 I2C-UHU at EXIST 2024: Transformer-Based Detection of Sexism and Source Intention in Memes Using a Learning with Disagreement Approach
A1 Carrillo Casado, Álvaro
A1 Román Pásaro, Javier
A1 Mata Vázquez, Jacinto
A1 Pachón Álvarez, Victoria
AB In this paper, the I2C-UHU Group addresses the Exist-2024 challenges of Sexism Identification and SourceIntention in Memes. We developed an ensemble of classifiers based on Transformer technology andadopted a Learning with Disagreement (LeWiDi) approach to analyze data from multiple annotators’perspectives. Techniques for constructing datasets and optimizing hyperparameters were explored,enhancing model performance through varied combinations. The optimal models were refined byweighting according to prediction accuracy. Our submissions for Task 4 achieved ranks of 4th withICM-Hard and ICM-Soft scores of 0.5668 and 0.4476, respectively. For Task 5, we secured 2nd and 10thplaces with ICM-Hard and ICM-Soft scores of 0.4119 and 0.2023, respectively.
PB CEUR-WS
SN 1613-0073
YR 2024
FD 2024
LK https://hdl.handle.net/10272/24658
UL https://hdl.handle.net/10272/24658
LA eng
NO Carrillo-Casado, A., Román-Pásaro, J., Mata-Vázquez, J., & Pachón-Álvarez, V. (2024). I2C-UHU at EXIST 2024: Transformer-Based Detection of Sexism and Source Intention in Memes Using a Learning with Disagreement Approach. CEUR Workshop Proceedings, 3740, 978-992.
NO This paper is part of the I+D+i Project titled “Conspiracy Theories and hate speech online:Comparison of patterns in narratives and social networks about COVID-19, immigrants,refugees and LGBTI people [NON-CONSPIRA-HATE!]”, PID2021-123983OB-I00, funded byMCIN/AEI/10.13039/501100011033/ and by “ERDF/EU”.
DS Repositorio Institucional de la Universidad de Huelva
RD 13 jul 2026