Detecting offensive language is a challenging task. Generalizing across different cultures and languages becomes even more challenging: besides lexical, syntactic and semantic differences, pragmatic aspects such as cultural norms and sensitivities, which are particularly relevant in this context, vary greatly. In this paper, we target Chinese offensive language detection and aim to investigate the impact of transfer learning using offensive language detection data from different cultural backgrounds, specifically Korean and English. We find that culture-specific biases in what is considered offensive negatively impact the transferability of language models (LMs) and that LMs trained on diverse cultural data are sensitive to different features in Chinese offensive language detection. In a few-shot learning scenario, however, our study shows promising prospects for non-English offensive language detection with limited resources. Our findings highlight the importance of cross-cultural transfer learning in improving offensive language detection and promoting inclusive digital spaces.

翻译：检测攻击性语言是一项具有挑战性的任务。在不同文化和语言之间进行泛化则更具挑战性：除了词汇、句法和语义差异外，语用层面（如文化规范和敏感性）在此背景下尤为相关，且差异显著。本文以中文攻击性语言检测为目标，旨在探究利用来自不同文化背景（特别是韩国和英语）的攻击性语言检测数据进行迁移学习的影响。我们发现，关于何为攻击行为的文化特定偏见会负面影响语言模型（LMs）的可迁移性，并且基于多样文化数据训练的LMs对中文攻击性语言检测中的不同特征具有敏感性。然而，在少样本学习场景下，我们的研究展示了在资源有限的情况下进行非英语攻击性语言检测的广阔前景。我们的发现强调了跨文化迁移学习在改进攻击性语言检测及促进包容性数字空间中的重要性。