当语义重叠不足以：土耳其和英语之间的跨语言婉转语转移
When Semantic Overlap Is Not Enough: Cross-Lingual Euphemism Transfer Between Turkish and English
AI总结：
本研究探讨了跨语言婉转语转移中的语义重叠不足问题，发现低资源环境下非重叠训练能提升性能，揭示了领域对齐对转移的影响。
摘要：
婉转语用于替代敏感的社会表达，通常通过软化或重新框架意义来实现，其依赖于文化和语用背景，这使得在不同语言之间建模变得复杂。在本研究中，我们探讨了跨语言等价性如何影响多语言婉转语检测中的转移。我们根据功能、语用和语义对土耳其和英语中的可能婉转语术语（PETs）进行分类，分为重叠（OPETs）和非重叠（NOPETs）子集。我们的发现揭示了转移的不对称性：语义重叠不足以保证积极的转移，尤其是在低资源的土耳其到英语方向，即使对于重叠的婉转语，性能也可能下降，在某些情况下，基于NOPET训练的性能反而会提高。标签分布的差异有助于解释这些反直觉的结果。类别级分析表明，转移可能受到领域特定对齐的影响，尽管证据受到稀疏性的限制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
