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The popularity of social media has created problems such as hate speech andsexism. The identification and classification of sexism in social media arevery relevant tasks, as they would allow building a healthier socialenvironment. Nevertheless, these tasks are considerably challenging. This workproposes a system to use multilingual and monolingual BERT and data pointstranslation and ensemble strategies for sexism identification andclassification in English and Spanish. It was conducted in the context of thesEXism Identification in Social neTworks shared 2021 (EXIST 2021) task,proposed by the Iberian Languages Evaluation Forum (IberLEF). The proposedsystem and its main components are described, and an in-depth hyperparametersanalysis is conducted. The main results observed were: (i) the system obtainedbetter results than the baseline model (multilingual BERT); (ii) ensemblemodels obtained better results than monolingual models; and (iii) an ensemblemodel considering all individual models and the best standardized valuesobtained the best accuracies and F1-scores for both tasks. This work obtainedfirst place in both tasks at EXIST, with the highest accuracies (0.780 for task1 and 0.658 for task 2) and F1-scores (F1-binary of 0.780 for task 1 andF1-macro of 0.579 for task 2).

Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models