Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification
Ashwin Geet d'Sa, Irina Illina, Dominique Fohr, Dietrich Klakow, Dana Ruiter · 2020
Research on hate speech classification has received increased attention.In real-life scenarios, a small amount of labeled hate speech data is available to train a reliable classifier.Semi-supervised learning takes advantage of a small amount of labeled data and a large amount of unlabeled data.In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification.The quality of labeling the unlabeled set depends on the input representations.In this work, we show that pre-trained representations are label agnostic, and when used with label propagation yield poor results.Neural network-based fine-tuning can be adopted to learn task-specific representations using a small amount of labeled data.We show that fully fine-tuned representations may not always be the best representations for the label propagation and intermediate representations may perform better in a semi-supervised setup.