Trade-offs in Cross-lingual Euphemism Detection: XLM-R vs. mBERT with Sequential and Simultaneous Fine-tuning

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: What is the trade-off between inference efficiency (measured in tokens/second) and accuracy in cross-lingual euphemism detection when using XLM-R vs. mBERT with sequential vs. simultaneous fine-tuning across five languages?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.