  The propagation of offensive content through social media channels has
garnered attention of the research community. Multiple works have proposed
various semantically related yet subtle distinct categories of offensive
speech. In this work, we explore meta-earning approaches to leverage the
diversity of offensive speech corpora to enhance their reliable and efficient
detection. We propose a joint embedding architecture that incorporates the
input's label and definition for classification via Prototypical Network. Our
model achieves at least 75% of the maximal F1-score while using less than 10%
of the available training data across 4 datasets. Our experimental findings
also provide a case study of training strategies valuable to combat resource
scarcity.
