Typological Similarity and Fine-Tuning Strategies in 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: Does the typological similarity between source and target languages mitigate the performance gap in cross-lingual euphemism detection when using simultaneous vs. sequential fine-tuning in models like XLM-R or mBERT?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.8/10.