  The prevalence of abusive language on different online platforms has been a
major concern that raises the need for automated cross-platform abusive
language detection. However, prior works focus on concatenating data from
multiple platforms, inherently adopting Empirical Risk Minimization (ERM)
method. In this work, we address this challenge from the perspective of domain
generalization objective. We design SCL-Fish, a supervised contrastive learning
integrated meta-learning algorithm to detect abusive language on unseen
platforms. Our experimental analysis shows that SCL-Fish achieves better
performance over ERM and the existing state-of-the-art models. We also show
that SCL-Fish is data-efficient and achieves comparable performance with the
large-scale pre-trained models upon finetuning for the abusive language
detection task.
