Layer-wise adaptation with typological features vs. adapter-based fine-tuning for 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 layer-wise adaptation strategy with typological features in XLM-R-Large compare to adapter-based fine-tuning (e.g., P-adapters, T-adapters) in terms of accuracy and inference efficiency for cross-lingual euphemism detection across high- and low-resource languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.8/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.8/10.