MEDs for PETs：面向潜在委婉语术语的多语言委婉语消歧
MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms
AI总结：
本研究针对多语言委婉语计算处理问题，训练XLM-RoBERTa模型实现PETs消歧，验证跨语言零样本学习及多语言模型的性能优势，并分析跨语言迁移与领域数据的关联。
摘要：
本研究针对委婉语这一普遍语言现象，探究其跨多语言的计算处理问题。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下对潜在委婉语术语（PETs）进行消歧。结合当前研究趋势，我们证实了跨语言零样本学习的有效性。研究还发现，在该任务上，多语言模型的表现优于单语言模型，且差异具有统计显著性，表明多语言数据为模型学习委婉语的跨语言计算特性提供了额外机会。在后续分析中，我们聚焦于死亡、身体机能等普遍的委婉语“类别”。为进一步理解跨语言迁移的本质，我们测试了同领域跨语言数据是否比其他领域的同语言数据更重要。
英文摘要：
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.
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。
