  The fast spread of hate speech on social media impacts the Internet
environment and our society by increasing prejudice and hurting people.
Detecting hate speech has aroused broad attention in the field of natural
language processing. Although hate speech detection has been addressed in
recent work, this task still faces two inherent unsolved challenges. The first
challenge lies in the complex semantic information conveyed in hate speech,
particularly the interference of insulting words in hate speech detection. The
second challenge is the imbalanced distribution of hate speech and non-hate
speech, which may significantly deteriorate the performance of models. To
tackle these challenges, we propose a novel dual contrastive learning (DCL)
framework for hate speech detection. Our framework jointly optimizes the
self-supervised and the supervised contrastive learning loss for capturing
span-level information beyond the token-level emotional semantics used in
existing models, particularly detecting speech containing abusive and insulting
words. Moreover, we integrate the focal loss into the dual contrastive learning
framework to alleviate the problem of data imbalance. We conduct experiments on
two publicly available English datasets, and experimental results show that the
proposed model outperforms the state-of-the-art models and precisely detects
hate speeches.
