  Reliable automatic hate speech (HS) detection systems must adapt to the
in-flow of diverse new data to curtail hate speech. However, hate speech
detection systems commonly lack generalizability in identifying hate speech
dissimilar to data used in training, impeding their robustness in real-world
deployments. In this work, we propose a hate speech generalization framework
that leverages emotion knowledge in a multitask architecture to improve the
generalizability of hate speech detection in a cross-domain setting. We
investigate emotion corpora with varying emotion categorical scopes to
determine the best corpus scope for supplying emotion knowledge to foster
generalized hate speech detection. We further assess the relationship between
using pretrained Transformers models adapted for hate speech and its effect on
our emotion-enriched hate speech generalization model. We perform extensive
experiments on six publicly available datasets sourced from different online
domains and show that our emotion-enriched HS detection generalization method
demonstrates consistent generalization improvement in cross-domain evaluation,
increasing generalization performance up to 18.1% and average cross-domain
performance up to 8.5%, according to the F1 measure.
