双类提示生成：通过数据增强提升印尼语基于性别的仇恨言论检测
Dual-Class Prompt Generation: Enhancing Indonesian Gender-Based Hate Speech Detection through Data Augmentation
AI总结：
本文提出双类提示生成数据增强方法，通过结合仇恨与非仇恨言论示例，有效缓解印尼语性别仇恨言论检测的类别不平衡与数据稀缺问题，在准确率和F1分数上达到最优。
摘要：
由于标注数据集有限，在印尼语社交媒体中检测基于性别的仇恨言论仍然具有挑战性。尽管二元仇恨言论分类已取得进展，但由于类别不平衡问题，像针对性别的仇恨言论这样更细粒度的类别仍研究不足。本文通过比较三种数据增强技术来解决印尼语基于性别的仇恨言论检测的这一空白。我们评估了回译、单类提示生成（仅使用仇恨言论示例）以及我们提出的双类提示生成（同时使用仇恨言论和非仇恨言论示例）。实验表明，所有增强方法均提高了分类性能，其中我们的双类方法取得了最佳结果（使用Random Forest达到88.5%的准确率，88.1%的F1分数）。语义相似性分析显示，双类提示生成产生了最新颖的内容，而T-SNE可视化证实这些样本占据了独特的特征空间区域，同时保持了类别特征。我们的研究结果表明，结合两个类别的示例有助于语言模型生成更多样化且具代表性的样本，有效解决了专业仇恨言论检测中数据有限的挑战。
英文摘要：
Detecting gender-based hate speech in Indonesian social media remains challenging due to limited labeled datasets. While binary hate speech classification has advanced, a more granular category like gender-targeted hate speech is understudied because of class imbalance issues. This paper addresses this gap by comparing three data augmentation techniques for Indonesian gender-based hate speech detection. We evaluate backtranslation, single-class prompt generation (using only hate speech examples), and our proposed dual-class prompt generation (using both hate speech and non-hate speech examples). Experiments show all augmentation methods improve classification performance, with our dual-class approach achieving the best results (88.5% accuracy, 88.1% F1-score using Random Forest). Semantic similarity analysis reveals dual-class prompt generation produces the most novel content, while T-SNE visualizations confirm these samples occupy distinct feature space regions while maintaining class characteristics. Our findings suggest that incorporating examples from both classes helps language models generate more diverse yet representative samples, effectively addressing limited data challenges in specialized hate speech detection.
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。
