@inproceedings{kohli-etal-2022-adversarial,
title = "Adversarial Perturbations Augmented Language Models for Euphemism Identification",
author = "Kohli, Guneet and
Kaur, Prabsimran and
Bedi, Jatin",
editor = "Ghosh, Debanjan and
Beigman Klebanov, Beata and
Muresan, Smaranda and
Feldman, Anna and
Poria, Soujanya and
Chakrabarty, Tuhin",
booktitle = "Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.flp-1.22",
doi = "10.18653/v1/2022.flp-1.22",
pages = "154--159",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Adversarial Perturbations Augmented Language Models for Euphemism Identification
%A Kohli, Guneet
%A Kaur, Prabsimran
%A Bedi, Jatin
%Y Ghosh, Debanjan
%Y Beigman Klebanov, Beata
%Y Muresan, Smaranda
%Y Feldman, Anna
%Y Poria, Soujanya
%Y Chakrabarty, Tuhin
%S Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F kohli-etal-2022-adversarial
%X 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.
%R 10.18653/v1/2022.flp-1.22
%U https://aclanthology.org/2022.flp-1.22
%U https://doi.org/10.18653/v1/2022.flp-1.22
%P 154-159
Markdown (Informal)
[Adversarial Perturbations Augmented Language Models for Euphemism Identification](https://aclanthology.org/2022.flp-1.22) (Kohli et al., Fig-Lang 2022)
ACL