基于邻域感知最优传输的知识传递算法在低资源仇恨语音检测中的应用
Transferring Knowledge via Neighborhood-Aware Optimal Transport for Low-Resource Hate Speech Detection
摘要：
在线平台上仇恨内容的令人担忧的增长增加了对自动仇恨言论检测的关注，通常被制定为监督分类任务。最先进的基于深度学习的方法通常需要大量的标记资源进行训练。然而，注释仇恨言论资源是昂贵的，耗时的，并且往往对注释者有害。这就迫切需要将知识从现有的标记资源转移到低资源仇恨言论语料库，以提高系统性能。在这方面，基于社区的框架已被证明是有效的。但灵活性有限。在我们的论文中，我们提出了一种新的训练策略，可以灵活地对从资源丰富的语料库中检索到的邻居的相对接近度进行建模，以学习迁移量。特别是，我们将邻域信息与最优传输，这允许利用数据嵌入空间的几何形状。通过调整邻居的联合嵌入和标签分布，我们在低资源情况下，在不同的公开可用的仇恨言论语料库上展示了强基线的实质性改进。
英文摘要：
The concerning rise of hateful content on online platforms has increased the attention towards automatic hate speech detection, commonly formulated as a supervised classification task. State-of-the-art deep learning-based approaches usually require a substantial amount of labeled resources for training. However, annotating hate speech resources is expensive, time-consuming, and often harmful to the annotators. This creates a pressing need to transfer knowledge from the existing labeled resources to low-resource hate speech corpora with the goal of improving system performance. For this, neighborhood-based frameworks have been shown to be effective. However, they have limited flexibility. In our paper, we propose a novel training strategy that allows flexible modeling of the relative proximity of neighbors retrieved from a resource-rich corpus to learn the amount of transfer. In particular, we incorporate neighborhood information with Optimal Transport, which permits exploiting the geometry of the data embedding space. By aligning the joint embedding and label distributions of neighbors, we demonstrate substantial improvements over strong baselines, in low-resource scenarios, on different publicly available hate speech corpora.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
