Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features
This work addresses the need for more culturally sensitive language technologies in machine learning, though it appears incremental in applying existing cultural analysis methods to a specific transfer learning context.
The study tackled the problem of predicting cross-cultural transfer learning success for offensive language detection by examining cultural features, finding that cultural value surveys can predict transfer learning effectiveness and that offensive word distance further improves prediction accuracy.
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.