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.

翻译：委婉语替代具有社会敏感性的表达，通常起到软化或重构语义的作用，其依赖文化和语用语境的特点使得跨语言建模变得复杂。本研究探讨跨语言等价性如何影响多语言委婉语检测中的迁移效果。我们根据功能、语用和语义的对齐程度，将土耳其语和英语中的潜在委婉语术语（PETs）划分为重叠（OPETs）与非重叠（NOPETs）子集。研究结果揭示了迁移的不对称性：语义重叠不足以保证正向迁移，尤其在资源匮乏的土耳其语到英语方向中，即使是重叠委婉语的检测性能也可能下降，而在某些情况下，基于NOPET的训练反而能带来提升。标签分布的差异有助于解释这些反直觉的结果。类别层面的分析表明，迁移可能受到领域特定对齐的影响，尽管稀疏性限制了证据的充分性。