This study investigates the computational processing of euphemisms, a
universal linguistic phenomenon, across multiple languages. We train a
multilingual transformer model (XLM-RoBERTa) to disambiguate potentially
euphemistic terms (PETs) in multilingual and cross-lingual settings. In line
with current trends, we demonstrate that zero-shot learning across languages
takes place. We also show cases where multilingual models perform better on the
task compared to monolingual models by a statistically significant margin,
indicating that multilingual data presents additional opportunities for models
to learn about cross-lingual, computational properties of euphemisms. In a
follow-up analysis, we focus on universal euphemistic "categories" such as
death and bodily functions among others. We test to see whether cross-lingual
data of the same domain is more important than within-language data of other
domains to further understand the nature of the cross-lingual transfer.

本文研究了多种语言中委婉语的计算处理。我们训练了一个多语言变换器模型（XLM-RoBERTa）来消除多语言和跨语言环境下的可能的委婉语词汇。与当前趋势一致，我们展示了跨语言的零射击学习的现象。我们还展示了多语言模型在这一任务上相对于单语言模型有显著优势的案例，表明多语言数据提供了额外的机会来了解委婉语的跨语言计算特性。在后续分析中，我们着重研究了普遍委婉语的 “类别”，如死亡和生理功能等。我们测试了同一领域的跨语言数据是否比不同领域的语言内数据更重要，以进一步了解跨语言转移的性质。