Abstract
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.- Anthology ID:
- 2022.flp-1.24
- Volume:
- Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Debanjan Ghosh, Beata Beigman Klebanov, Smaranda Muresan, Anna Feldman, Soujanya Poria, Tuhin Chakrabarty
- Venue:
- Fig-Lang
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 167–172
- Language:
- URL:
- https://aclanthology.org/2022.flp-1.24
- DOI:
- 10.18653/v1/2022.flp-1.24
- Cite (ACL):
- Sedrick Scott Keh. 2022. Exploring Euphemism Detection in Few-Shot and Zero-Shot Settings. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), pages 167–172, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Exploring Euphemism Detection in Few-Shot and Zero-Shot Settings (Keh, Fig-Lang 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.flp-1.24.pdf