Impact of Typological Similarity on Cross-Lingual Euphemism Detection Performance

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 typological similarity between language pairs on cross-lingual transfer performance in euphemism detection, as measured by accuracy and F1-score when sequentially fine-tuning XLM-R on pairs like English-Turkish (high similarity) vs. Chinese-Yoruba (low similarity)?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.