Rohit Bharadwaj


EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentation
Sedrick Scott Keh | Rohit Bharadwaj | Emmy Liu | Simone Tedeschi | Varun Gangal | Roberto Navigli
Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)

We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection. We (1) identify and correct potentially mislabelled rows in the dataset, (2) curate an expanded corpus called EuphAug, (3) leverage model representations of Potentially Euphemistic Terms (PETs), and (4) explore using representations of semantically close sentences to aid in classification. Using our augmented dataset and kNN-based methods, EUREKA was able to achieve state-of-the-art results on the public leaderboard of the Euphemism Detection Shared Task, ranking first with a macro F1 score of 0.881.