  Sexism, an injustice that subjects women and girls to enormous suffering,
manifests in blatant as well as subtle ways. In the wake of growing
documentation of experiences of sexism on the web, the automatic categorization
of accounts of sexism has the potential to assist social scientists and policy
makers in studying and countering sexism better. The existing work on sexism
classification, which is different from sexism detection, has certain
limitations in terms of the categories of sexism used and/or whether they can
co-occur. To the best of our knowledge, this is the first work on the
multi-label classification of sexism of any kind(s), and we contribute the
largest dataset for sexism categorization. We develop a neural solution for
this multi-label classification that can combine sentence representations
obtained using models such as BERT with distributional and linguistic word
embeddings using a flexible, hierarchical architecture involving recurrent
components and optional convolutional ones. Further, we leverage unlabeled
accounts of sexism to infuse domain-specific elements into our framework. The
best proposed method outperforms several deep learning as well as traditional
machine learning baselines by an appreciable margin.
