Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive
  Language Detection
Gretel Liz De la Peña Sarracén Paolo Rosso Robert Litschko Goran Glavaš Simone Paolo Ponzetto
Cross-lingual transfer learning from high-resource to medium and low-resource
languages has shown encouraging results. However, the scarcity of resources in
target languages remains a challenge. In this work, we resort to data
augmentation and continual pre-training for domain adaptation to improve
cross-lingual abusive language detection. For data augmentation, we analyze two
existing techniques based on vicinal risk minimization and propose MIXAG, a
novel data augmentation method which interpolates pairs of instances based on
the angle of their representations. Our experiments involve seven languages
typologically distinct from English and three different domains. The results
reveal that the data augmentation strategies can enhance few-shot cross-lingual
abusive language detection. Specifically, we observe that consistently in all
target languages, MIXAG improves significantly in multidomain and multilingual
environments. Finally, we show through an error analysis how the domain
adaptation can favour the class of abusive texts (reducing false negatives),
but at the same time, declines the precision of the abusive language detection
model.