社交媒体中的鲁棒仇恨言论检测：一项跨数据集实证评估
Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation
AI总结：
本文通过大规模跨数据集实验，发现组合多个仇恨言论检测数据集微调语言模型，可显著提升模型鲁棒性和泛化能力，优于任何单一数据集。
摘要：
在线仇恨言论的自动检测是自然语言处理（NLP）中一个活跃的研究领域。迄今为止，大多数研究都基于社交媒体数据集，这些数据集有助于创建在其上训练的仇恨言论检测模型。然而，数据创建过程包含其自身的偏差，模型会固有地从这些数据集特定的偏差中学习。在本文中，我们进行了一项大规模的跨数据集比较，在不同的仇恨言论检测数据集上微调语言模型。该分析表明，当用作训练数据时，某些数据集比其他数据集更具泛化性。至关重要的是，我们的实验表明，结合仇恨言论检测数据集可以有助于开发鲁棒的仇恨言论检测模型。即使在控制数据规模并与最佳单个数据集进行比较时，这种鲁棒性仍然成立。
英文摘要：
The automatic detection of hate speech online is an active research area in NLP. Most of the studies to date are based on social media datasets that contribute to the creation of hate speech detection models trained on them. However, data creation processes contain their own biases, and models inherently learn from these dataset-specific biases. In this paper, we perform a large-scale cross-dataset comparison where we fine-tune language models on different hate speech detection datasets. This analysis shows how some datasets are more generalisable than others when used as training data. Crucially, our experiments show how combining hate speech detection datasets can contribute to the development of robust hate speech detection models. This robustness holds even when controlling by data size and compared with the best individual datasets.
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。
