  Detecting hate speech, especially in low-resource languages, is a non-trivial
challenge. To tackle this, we developed a tailored architecture based on
frozen, pre-trained Transformers to examine cross-lingual zero-shot and
few-shot learning, in addition to uni-lingual learning, on the HatEval
challenge data set. With our novel attention-based classification block AXEL,
we demonstrate highly competitive results on the English and Spanish subsets.
We also re-sample the English subset, enabling additional, meaningful
comparisons in the future.
