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

Li Zhou, Antonia Karamolegkou, Wenyu Chen, Daniel Hershcovich

TL;DR

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.

Abstract

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.

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

TL;DR

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.

Abstract

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.
Paper Structure (42 sections, 3 equations, 5 figures, 10 tables)

This paper contains 42 sections, 3 equations, 5 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. OLD methods.
  4. Optimal transfer language prediction.
  5. Cultural features.
  6. How to predict optimal transfer datasets?
  7. What features can be used to predict?
  8. Cultural-Dimension Features
  9. Domain-Specific Feature
  10. Features Analysis
  11. Cultural dimensions.
  12. Offensive distance.
  13. Culture vs. geography.
  14. Can we predict transfer languages for OLD successfully?
  15. Experimental Settings
  16. ...and 27 more sections

Figures (5)

  • Figure 1: T-SNE Visualization of Cultural Values: Countries represented as data points, with color-coded regions highlighting similarities and differences in cultural dimensions.
  • Figure 2: Network based on Offensive Word Distance. Edges connect languages based on their top-2 closest rankings. Solid lines represent mutual ranking, while dashed lines represent exclusive ranking.
  • Figure 3: Relative losses (in percent) from zero-shot transfer learning models over the intra-cultural models.
  • Figure 4: A pairplot showcasing the relationship between culturally relevant features using kernel density estimation (KDE), accompanied by correlation coefficients (in red digit) annotated on the corresponding scatter plots. The diagonal plots display the distribution of each feature using KDE curves, while the lower triangle depicts the joint distributions and contour plots using a color gradient (Blues colormap).
  • Figure 5: Fine-grained correlation analysis of dimension features in CulDim.