  In this paper, we propose a methodology for task 10 of SemEval23, focusing on
detecting and classifying online sexism in social media posts. The task is
tackling a serious issue, as detecting harmful content on social media
platforms is crucial for mitigating the harm of these posts on users. Our
solution for this task is based on an ensemble of fine-tuned transformer-based
models (BERTweet, RoBERTa, and DeBERTa). To alleviate problems related to class
imbalance, and to improve the generalization capability of our model, we also
experiment with data augmentation and semi-supervised learning. In particular,
for data augmentation, we use back-translation, either on all classes, or on
the underrepresented classes only. We analyze the impact of these strategies on
the overall performance of the pipeline through extensive experiments. while
for semi-supervised learning, we found that with a substantial amount of
unlabelled, in-domain data available, semi-supervised learning can enhance the
performance of certain models. Our proposed method (for which the source code
is available on Github attains an F1-score of 0.8613 for sub-taskA, which
ranked us 10th in the competition
