  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.
