Diana Cuevas Plancarte


2024

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MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms
Patrick Lee | Alain Chirino Trujillo | Diana Cuevas Plancarte | Olumide Ojo | Xinyi Liu | Iyanuoluwa Shode | Yuan Zhao | Anna Feldman | Jing Peng
Findings of the Association for Computational Linguistics: EACL 2024

Euphemisms are found across the world’s languages, making them a universal linguistic phenomenon. As such, euphemistic data may have useful properties for computational tasks across languages. In this study, we explore this premise by training 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.