  Offensive language detection is a crucial task in today's digital landscape,
where online platforms grapple with maintaining a respectful and inclusive
environment. However, building robust offensive language detection models
requires large amounts of labeled data, which can be expensive and
time-consuming to obtain. Semi-supervised learning offers a feasible solution
by utilizing labeled and unlabeled data to create more accurate and robust
models. In this paper, we explore a few different semi-supervised methods, as
well as data augmentation techniques. Concretely, we implemented eight
semi-supervised methods and ran experiments for them using only the available
data in the RO-Offense dataset and applying five augmentation techniques before
feeding the data to the models. Experimental results demonstrate that some of
them benefit more from augmentations than others.
