Dandys-de-BERTganim at EXIST 2025: a Multi-task Learning Architecture for Sexism Identification

This paper presents Dandys, our system developed for the EXIST 2025 shared task on sexism identification in social media at CLEF 2025. The challenge comprises three interconnected tasks: binary sexism detection, source intention classification, and sexism type categorization. To address these tasks, we adopt a multi-task learning architecture with language-specific transformers for English and Spanish tweets, integrating demographic information from annotators as contextual signals. We enhance model generalization through data augmentation techniques such as back-translation and a punctuation-based augmentation method. Furthermore, we introduce a soft-labeling data reader to better reflect annotation disagreement, aligning with the Learning with Disagreement paradigm. Our results demonstrate the effectiveness of leveraging task interdependence, soft supervision, and multilingual modeling for addressing complex sociolinguistic classification problems. Given good performance across all tasks, our architecture is validated as both robust and effective.