Multi-task learning neural framework for categorizing sexism

Sexism, a form of oppression based on one’s sex, manifests itself in numerous ways and causes enormous suffering. In view of the growing number of experiences of sexism reported online, automatically classifying these recollections can aid in the battle against sexism by allowing gender studies researchers and government officials involved in policymaking to conduct more effective analyses. This paper investigates the 23-class fine-grained, multi-label classification of accounts (reports) of sexism. Moreover, we propose a knowledge-based cascaded multi-task framework for fine-grained multi-label sexism classification. We leverage several supporting tasks, including homogeneous and heterogeneous auxiliary tasks. Homogeneous tasks are set up without incurring any manual labeling cost and heterogeneous tasks are set up which have a high correlation with the accounts of sexism. Unlabeled accounts of sexism are utilized through unsupervised learning to help construct our multi-task setup. In addition, we incorporate a knowledge module within the framework to infuse external knowledge features into the learning process. Further, we investigate transfer learning that employs weakly labeled accounts of sexism and transfers the learning to the multi-label sexism classification. We also devise objective functions that exploit label correlations in the training data explicitly. Multiple proposed multi-task methods outperform the state-of-the-art multi-label sexism classification across five standard metrics. •Propose a knowledge-based cascaded multi-task neural framework for the multi-label sexism classification.•Explore two types of auxiliary tasks: (a) Homogeneous tasks and (b) Heterogeneous tasks•Trained the model with weakly labeled data and transfered the knowledge to the sexism classification task.•Proposed loss functions aimed at tapping label correlations in the multi-label data explicitly.