How does the integration of visual context (emoji/image embeddings) into XLM-R-Base affect cross-lingual euphemism detection

Euphemisms are culturally variable and often ambiguous, posing challenges for language models, especially in low-resource settings. This paper investigates how cross-lingual transfer via sequential fine-tuning affects euphemism detection across five languages: English, Spanish, Chinese, Turkish, and Yoruba. We compare sequential fine-tuning with monolingual and simultaneous fine-tuning using XLM-R and mBERT, analyzing how performance is shaped by language pairings, typological features, and pretraining coverage. Results show that sequential fine-tuning with a high-resource L1 improves L2 perfo Research goal: How does the integration of visual context (emoji/image embeddings) into XLM-R-Base affect cross-lingual euphemism detection performance on XTREME-R, particularly when fine-tuned on high-resource languages (English, Spanish) and evaluated on Yoruba, as measured by F1 score improvements compared to text-only fine-tuning? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.5/10.