  We introduce HATELEXICON, a lexicon of slurs and targets of hate speech for
the countries of Brazil, Germany, India and Kenya, to aid training and
interpretability of models. We demonstrate how our lexicon can be used to
interpret model predictions, showing that models developed to classify extreme
speech rely heavily on target words when making predictions. Further, we
propose a method to aid shot selection for training in low-resource settings
via HATELEXICON. In few-shot learning, the selection of shots is of paramount
importance to model performance. In our work, we simulate a few-shot setting
for German and Hindi, using HASOC data for training and the Multilingual
HateCheck (MHC) as a benchmark. We show that selecting shots based on our
lexicon leads to models performing better on MHC than models trained on shots
sampled randomly. Thus, when given only a few training examples, using our
lexicon to select shots containing more sociocultural information leads to
better few-shot performance.
