Label-aware Hard Negative Sampling Strategies with Momentum Contrastive Learning for Implicit Hate Speech Detection
<|reference_start|>Label-aware Hard Negative Sampling Strategies with Momentum Contrastive Learning for Implicit Hate Speech Detection: 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<|reference_end|>
@article{kim2024label-aware,
    title={Label-aware Hard Negative Sampling Strategies with Momentum Contrastive
  Learning for Implicit Hate Speech Detection},
    author={Jaehoon Kim, Seungwan Jin, Sohyun Park, Someen Park, Kyungsik Han},
    journal={arXiv preprint arXiv:2406.07886},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07886},
    primaryClass={cs.CL}
}