Sequential Fine-Tuning Task Quantity and Zero-Shot Cross-Lingual Transfer in XTREME-R

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 number of intermediate tasks in sequential fine-tuning affect zero-shot cross-lingual transfer accuracy on the XTREME-R benchmark for low-resource languages?

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