  Detecting implicit hate speech that is not directly hateful remains a
challenge. Recent research has attempted to detect implicit hate speech by
applying contrastive learning to pre-trained language models such as BERT and
RoBERTa, but the proposed models still do not have a significant advantage over
cross-entropy loss-based learning. We found that contrastive learning based on
randomly sampled batch data does not encourage the model to learn hard negative
samples. In this work, we propose Label-aware Hard Negative sampling strategies
(LAHN) that encourage the model to learn detailed features from hard negative
samples, instead of naive negative samples in random batch, using
momentum-integrated contrastive learning. LAHN outperforms the existing models
for implicit hate speech detection both in- and cross-datasets. The code is
available at https://github.com/Hanyang-HCC-Lab/LAHN
