Cultural Compass: Predicting Transfer Learning Success in Offensive
  Language Detection with Cultural Features
Li Zhou Antonia Karamolegkou Wenyu Chen Daniel Hershcovich
The increasing ubiquity of language technology necessitates a shift towards
considering cultural diversity in the machine learning realm, particularly for
subjective tasks that rely heavily on cultural nuances, such as Offensive
Language Detection (OLD). Current understanding underscores that these tasks
are substantially influenced by cultural values, however, a notable gap exists
in determining if cultural features can accurately predict the success of
cross-cultural transfer learning for such subjective tasks. Addressing this,
our study delves into the intersection of cultural features and transfer
learning effectiveness. The findings reveal that cultural value surveys indeed
possess a predictive power for cross-cultural transfer learning success in OLD
tasks and that it can be further improved using offensive word distance. Based
on these results, we advocate for the integration of cultural information into
datasets. Additionally, we recommend leveraging data sources rich in cultural
information, such as surveys, to enhance cultural adaptability. Our research
signifies a step forward in the quest for more inclusive, culturally sensitive
language technologies.