MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially
  Euphemistic Terms
Patrick Lee Alain Chirino Trujillo Diana Cuevas Plancarte Olumide Ebenezer Ojo Xinyi Liu Iyanuoluwa Shode Yuan Zhao Jing Peng Anna Feldman
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.