Sequential Fine-Tuning Language Variation in Zero-Shot Euphemism Detection

Abstract

Abstract: 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 Question

What is the effect of varying the number of languages in sequential fine-tuning (e.g., 2 vs. 5 languages) on zero-shot cross-lingual accuracy for euphemism detection in low-resource languages, as measured on XTREME-R benchmarks?

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