Cross-lingual Transfer Effects on Euphemism Detection in Low-Resource Languages

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: To what extent does cross-lingual transfer via sequential fine-tuning improve F1-scores for euphemism detection in low-resource languages like Yoruba when evaluated on multilingual benchmark datasets such as XNLI or PAWS-X?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.3/10.