  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.
