  Through anonymisation and accessibility, social media platforms have
facilitated the proliferation of hate speech, prompting increased research in
developing automatic methods to identify these texts. This paper explores the
classification of sexism in text using a variety of deep neural network model
architectures such as Long-Short-Term Memory (LSTMs) and Convolutional Neural
Networks (CNNs). These networks are used in conjunction with transfer learning
in the form of Bidirectional Encoder Representations from Transformers (BERT)
and DistilBERT models, along with data augmentation, to perform binary and
multiclass sexism classification on the dataset of tweets and gabs from the
sEXism Identification in Social neTworks (EXIST) task in IberLEF 2021. The
models are seen to perform comparatively to those from the competition, with
the best performances seen using BERT and a multi-filter CNN model. Data
augmentation further improves these results for the multi-class classification
task. This paper also explores the errors made by the models and discusses the
difficulty in automatically classifying sexism due to the subjectivity of the
labels and the complexity of natural language used in social media.
