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The increasing ubiquity of language technology necessitates a shift towardsconsidering cultural diversity in the machine learning realm, particularly forsubjective tasks that rely heavily on cultural nuances, such as OffensiveLanguage Detection (OLD). Current understanding underscores that these tasksare substantially influenced by cultural values, however, a notable gap existsin determining if cultural features can accurately predict the success ofcross-cultural transfer learning for such subjective tasks. Addressing this,our study delves into the intersection of cultural features and transferlearning effectiveness. The findings reveal that cultural value surveys indeedpossess a predictive power for cross-cultural transfer learning success in OLDtasks and that it can be further improved using offensive word distance. Basedon these results, we advocate for the integration of cultural information intodatasets. Additionally, we recommend leveraging data sources rich in culturalinformation, such as surveys, to enhance cultural adaptability. Our researchsignifies a step forward in the quest for more inclusive, culturally sensitivelanguage technologies.

Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features