Stanford MLab at SemEval-2023 Task 10: Exploring GloVe- and
  Transformer-Based Methods for the Explainable Detection of Online Sexism
Hee Jung Choi Trevor Chow Aaron Wan Hong Meng Yam Swetha Yogeswaran Beining Zhou
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, we
perform three classification tasks to predict whether the text is sexist and
classify the sexist text into subcategories in order to provide an additional
explanation as to why the text is sexist. We explored many different types of
models, including GloVe embeddings as the baseline approach, transformer-based
deep learning models like BERT, RoBERTa, and DeBERTa, ensemble models, and
model blending. We explored various data cleaning and augmentation methods to
improve model performance. Pre-training transformer models yielded significant
improvements in performance, and ensembles and blending slightly improved
robustness in the F1 score.