  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.
