  This work builds upon the Euphemism Detection Shared Task proposed in the
EMNLP 2022 FigLang Workshop, and extends it to few-shot and zero-shot settings.
We demonstrate a few-shot and zero-shot formulation using the dataset from the
shared task, and we conduct experiments in these settings using RoBERTa and
GPT-3. Our results show that language models are able to classify euphemistic
terms relatively well even on new terms unseen during training, indicating that
it is able to capture higher-level concepts related to euphemisms.
