XLM-R Fine-Tuning Strategies for Cross-Lingual Euphemism Detection: Throughput-Accuracy Trade-offs

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 effect of varying the number of fine-tuning steps in sequential vs. simultaneous fine-tuning on the inference efficiency of XLM-R for cross-lingual euphemism detection, measured by throughput and accuracy trade-offs?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.