Cross-lingual Euphemism Detection via Typologically Diverse Intermediate 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: How does the inclusion of typologically diverse languages (e.g., Turkish, Chinese) in intermediate fine-tuning steps influence the cross-lingual generalization of euphemism detection in low-resource languages like Yoruba, as measured by accuracy on XTREME-R and cross-domain F1 score on SEED-Bench?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.