近日，实验室硕士生卢俊宇关于仇恨言论检测识别的研究成果被语言信号处理领域顶级期刊IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 录用。TASLP属于中科院一区、CCF推荐B类以及清华计算机学科推荐A类期刊。

题目：Hate Speech Detection via Dual Contrastive Learning（基于双重对比学习的仇恨言论检测）

摘要：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.

  仇恨言论在社交媒体上的肆虐影响了互联网和乃至社会环境，加剧了偏见，损害了用户的利益。仇恨言论检测在自然语言处理领域引起了广泛关注。尽管现有的检测方法取得了一定的效果，但仇恨言论检测任务仍然面临着两个尚未解决的固有挑战。第一个挑战在于仇恨言论中传达的复杂语义信息对仇恨言论检测的干扰，尤其是侮辱性词语的模型性能的影响。第二个挑战是数据集中仇恨言论和非仇恨言论的不平衡分布，这可能会大大降低模型的检测效果。为了应对这些挑战，我们提出了一种新颖的双重对比学习（DCL）框架，用于仇恨言论检测的任务中。我们的框架联合优化了自监督和有监督对比学习损失，使模型更好地捕捉超越token-level语义之外的span-level信息，并特别关注了包含辱骂和侮辱性词语的样本。此外，我们将focal loss整合到DCL框架中，以缓解数据不平衡问题。我们在两个公开的英语数据集上进行了实验，实验结果表明，所提出的模型优于最先进的模型，能够精确地检测出仇恨言论。