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.

翻译：本研究探讨了委婉语这一普遍语言现象在多语言环境下的计算处理。我们训练了一个多语言Transformer模型（XLM-RoBERTa），用于在多语言和跨语言场景中消除潜在委婉术语（PETs）的歧义。顺应当前研究趋势，我们验证了跨语言的零样本学习能力。同时，结果表明，多语言模型在该任务上的表现显著优于单语模型，这说明多语言数据为模型学习跨语言委婉语的计算特性提供了更多机会。在后续分析中，我们聚焦于死亡、身体功能等通用委婉语“类别”，并测试了同一领域的跨语言数据是否比同一语言其他领域的数据更重要，以进一步理解跨语言迁移的本质。