Abstract
Euphemisms are mild words or expressions used instead of harsh or direct words while talking to someone to avoid discussing something unpleasant, embarrassing, or offensive. However, they are often ambiguous, thus making it a challenging task. The Third Workshop on Figurative Language Processing, colocated with EMNLP 2022 organized a shared task on Euphemism Detection to better understand euphemisms. We have used the adversarial augmentation technique to construct new data. This augmented data was then trained using two language models: BERT and longformer. To further enhance the overall performance, various combinations of the results obtained using longformer and BERT were passed through a voting ensembler. We achieved an F1 score of 71.5 using the combination of two adversarial longformers, two adversarial BERT, and one non-adversarial BERT.- Anthology ID:
- 2022.flp-1.22
- 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:
- 154–159
- Language:
- URL:
- https://aclanthology.org/2022.flp-1.22
- DOI:
- 10.18653/v1/2022.flp-1.22
- Cite (ACL):
- Guneet Kohli, Prabsimran Kaur, and Jatin Bedi. 2022. Adversarial Perturbations Augmented Language Models for Euphemism Identification. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), pages 154–159, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Adversarial Perturbations Augmented Language Models for Euphemism Identification (Kohli et al., Fig-Lang 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.flp-1.22.pdf