Papers › Exploring Euphemism Detection in Few-Shot and Zero-Shot Settings

Exploring Euphemism Detection in Few-Shot and Zero-Shot Settings

24 Oct 2022arXiv:2210.12926archive 2025-07-28

Sedrick Scott Keh

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.

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Adam Attention Attention Dropout BERT BPE Cosine Annealing Dense Connections Dropout GPT-3 Layer Normalization Linear Layer Linear Warmup With Cosine Annealing Linear Warmup With Linear Decay Multi-Head Attention Residual Connection RoBERTa Softmax Weight Decay WordPiece

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