  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. Our code is
available at https://github.com/sedrickkeh/EUREKA.
