Identification of Racial and Sexist Stereotypes in Spanish: A Learning with Disagreements Approach

Resumen

Hate speech has proliferated significantly in recent years, largely driven by the widespread adoption of social media platforms. Hate speech often operates implicitly, leveraging subtle stereotypes to propagate discriminatory views. These covert mechanisms allow harmful content to disguise itself, making detection increasingly complex. As a result, tackling hate speech has become an urgent priority, driving the widespread adoption of deep learning models to detect and combat harmful content. Given the inherently subjective nature of hate speech and its nuanced manifestations, there is a need to develop models that are as generalizable as possible. This has led to the emergence of the learning with disagreements paradigm, which aims to introduce disagreements within the task itself to enhance model generalizability. This paper investigates the latter paradigm through two shared tasks. The first task, DETEST-Dis, explores stereotypes against immigrants in online comments and was organized at IberLEF 2024. Our results are among the best of all participating teams, surpassing traditional approaches. The second task, EXIST, focuses on sexism in memes and was organized at CLEF 2024. Here, our performance is enhanced by adding features from an external model as well as data augmentation. Our source code can be found on https://github.com/Buzzeitor30/DETESTS-DIS and https://github.com/Buzzeitor30/EXIST-TFM.