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Detecting hate speech, especially in low-resource languages, is a non-trivialchallenge. To tackle this, we developed a tailored architecture based onfrozen, pre-trained Transformers to examine cross-lingual zero-shot andfew-shot learning, in addition to uni-lingual learning, on the HatEvalchallenge 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, meaningfulcomparisons in the future.

Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL