Cross-Cultural Transfer Learning for Chinese Offensive Language Detection
This work addresses the challenge of offensive language detection in non-English languages, which is important for promoting inclusive digital spaces, but it is incremental as it builds on existing transfer learning methods.
The paper tackled the problem of detecting offensive language in Chinese by investigating cross-cultural transfer learning using data from Korean and English, finding that culture-specific biases reduce transferability but few-shot learning shows promise for resource-limited scenarios.
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.