When Does Language Transfer Help? Sequential Fine-Tuning for Cross-Lingual Euphemism Detection

TL;DR

This study explores how sequential fine-tuning enhances cross-lingual euphemism detection in multilingual models, especially benefiting low-resource languages by leveraging high-resource language data.

Contribution

It demonstrates that sequential fine-tuning with high-resource languages improves performance on low-resource languages, providing insights into transfer dynamics and model sensitivities.

Findings

Sequential fine-tuning boosts low-resource language performance.

XLM-R outperforms mBERT but is more sensitive to pretraining gaps.

Sequential fine-tuning is an effective strategy for multilingual euphemism detection.

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 performance, especially for low-resource languages like Yoruba and Turkish. XLM-R achieves larger gains but is more sensitive to pretraining gaps and catastrophic forgetting, while mBERT yields more stable, though lower, results. These findings highlight sequential fine-tuning as a simple yet effective strategy for improving euphemism detection in multilingual models, particularly when low-resource languages are involved.