When Semantic Overlap Is Not Enough: Cross-Lingual Euphemism Transfer Between Turkish and English
This addresses the problem of modeling culturally dependent euphemisms for multilingual NLP, but it is incremental as it builds on existing transfer learning approaches.
The study tackled cross-lingual euphemism detection between Turkish and English, finding that semantic overlap does not ensure positive transfer, with performance degrading in low-resource Turkish-to-English direction and sometimes improving with non-overlapping terms.
Euphemisms substitute socially sensitive expressions, often softening or reframing meaning, and their reliance on cultural and pragmatic context complicates modeling across languages. In this study, we investigate how cross-lingual equivalence influences transfer in multilingual euphemism detection. We categorize Potentially Euphemistic Terms (PETs) in Turkish and English into Overlapping (OPETs) and Non-Overlapping (NOPETs) subsets based on their functional, pragmatic, and semantic alignment. Our findings reveal a transfer asymmetry: semantic overlap is insufficient to guarantee positive transfer, particularly in low-resource Turkish-to-English direction, where performance can degrade even for overlapping euphemisms, and in some cases, improve under NOPET-based training. Differences in label distribution help explain these counterintuitive results. Category-level analysis suggests that transfer may be influenced by domain-specific alignment, though evidence is limited by sparsity.