Adversarial Perturbations Augmented Language Models for Euphemism Identification

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 co-located 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, namely, 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 were able to achieve an F1 score of 71.5 using the combination of two adversarial Longformers, two ad- versarial BERT, 1 non adversarial BERT.