XLM-R Euphemism Detection via Sequential Fine-Tuning Across Typologically Diverse Language Pairings

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: What is the impact of typologically diverse language pairings (e.g., English-Turkish vs. Yoruba-Chinese) on the F1 score performance of XLM-R when using sequential fine-tuning for euphemism detection, compared to monolingual or simultaneous multilingual fine-tuning strategies?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.