论文标题
使用冷冻变压器语言模型和AXEL的跨语言零和几次仇恨言论检测
Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL
论文作者
论文摘要
检测仇恨言论，尤其是在低资源语言中，是一个非平凡的挑战。为了解决这个问题，我们开发了一种基于冷冻的，预先训练的变压器的量身定制的体系结构，以检查跨语义的零射击和几乎没有射击的学习，除了单语学习外，还基于Hateval挑战数据集。借助我们新颖的基于注意力的分类块Axel，我们在英语和西班牙子集上展示了竞争激烈的结果。我们还将重新置换英文子集，从而实现未来的其他有意义的比较。
Detecting hate speech, especially in low-resource languages, is a non-trivial challenge. To tackle this, we developed a tailored architecture based on frozen, pre-trained Transformers to examine cross-lingual zero-shot and few-shot learning, in addition to uni-lingual learning, on the HatEval challenge data set. With our novel attention-based classification block AXEL, we demonstrate highly competitive results on the English and Spanish subsets. We also re-sample the English subset, enabling additional, meaningful comparisons in the future.
