FEED PETs：潜在委婉语消歧的进一步实验与扩展
FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms
AI总结：
本文扩展潜在委婉语消歧任务，标注 PET 模糊性并分析模型表现差异，同时构建约鲁巴语、西班牙语和汉语普通话语料库，用 mBERT 与 XLM-RoBERTa 建立多语言初步基准。
摘要：
Transformer 已被证明在英语委婉语消歧任务中表现良好，该任务要求在特定语境中将潜在委婉语（PET）分类为委婉或非委婉。在本研究中，我们从两个方面扩展该任务。首先，我们对 PET 的模糊性进行标注，模糊性是一种与委婉语相关的语言属性，并发现 Transformer 通常更擅长分类模糊的 PET，这表明数据中存在影响性能的语言差异。其次，我们提出了三种不同语言的新型委婉语语料库：约鲁巴语、西班牙语和汉语普通话。我们使用多语言 Transformer 模型 mBERT 和 XLM-RoBERTa 在每种语言中开展委婉语消歧实验，建立了可用于启动未来工作的初步结果。
英文摘要：
Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work.
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。
