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In this paper, we discuss the methods we applied at SemEval-2023 Task 10:Towards the Explainable Detection of Online Sexism. Given an input text, weperform three classification tasks to predict whether the text is sexist andclassify the sexist text into subcategories in order to provide an additionalexplanation as to why the text is sexist. We explored many different types ofmodels, including GloVe embeddings as the baseline approach, transformer-baseddeep learning models like BERT, RoBERTa, and DeBERTa, ensemble models, andmodel blending. We explored various data cleaning and augmentation methods toimprove model performance. Pre-training transformer models yielded significantimprovements in performance, and ensembles and blending slightly improvedrobustness in the F1 score.

Stanford MLab at SemEval-2023 Task 10: Exploring GloVe- and Transformer-Based Methods for the Explainable Detection of Online Sexism