Improving Cross-Domain Hate Speech Generalizability with Emotion Knowledge
This work addresses the robustness of hate speech detection for real-world deployments by improving cross-domain generalizability, though it is incremental as it builds on existing multitask and emotion-based methods.
The paper tackled the problem of hate speech detection systems lacking generalizability to new domains by proposing a framework that leverages emotion knowledge in a multitask architecture, resulting in up to 18.1% improvement in generalization performance and an average cross-domain performance increase of 8.5% in F1 score.
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.