Adversarial Training for Robust Euphemism Detection in Low-Resource Languages

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: Does incorporating adversarial training during sequential fine-tuning improve robustness in euphemism detection for low-resource languages, and how does this compare to standard fine-tuning in terms of XTREME-R accuracy and inference efficiency?

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