EUREKA : EU phemism Recognition Enhanced through Knn-based methods and Augmentation (2022)

Abstract: 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.

## introduction euphemisms are mild or indirect expressions used in place of harsher or more direct ones. in everyday speech, euphemisms function as a means to politely discuss taboo or sensitive topics @xcite , to downplay certain situations @xcite , or to mask intent @xcite . the euphemism detection task is a key stepping stone to developing natural language systems that are able to process @xcite @xcite and generate non-literal texts. in this paper, we detail our methods to the euphemism detection shared task at the emnlp 2022 figlang workshop 2 . we achieve performance improvements on two fronts: using these methods, we develop a system called eureka which achieves a macro f1 score of 0.881 on the public leaderboard and ranks first among all submissions. we found the data innovations to be more significant in our case, indicating that euphemistic terms can be classified with some accuracy if potentially euphemistic spans are identified earlier in the pipeline. ## methodology for our baseline model, we use a pretrained roberta-large model @xcite . for evaluation, we use the macro f1-score, as specified by the shared task description. ## related work euphemism detection is a relatively underexplored task. in this paper, we use the euphemism pet dataset gathered by @xcite . @xcite also use this dataset, but for the task of extracting pets from a given sentence. in the past, other methods have focused specifically on certain types of euphemisms, such as drugs @xcite , firing/lying/stealing @xcite , and hate speech @xcite . below, we further detail some of the methods and techniques previously explored in this area. @xcite use bert and the masked language model objective to create candidate euphemisms based on input target keywords of sensitive topics. zhu and bhat (2021) extend this to multi-word euphemistic phrases using spanbert. similar to the previous paper, they also generate and filter a list of euphemistic phrase candidates, then rank these candidates using probabilities from the masked language model. meanwhile, felt and riloff (2020) use sentiment analysis methods to detect euphemisms, exploring various properties associated with sentiment such as affective polarity, connotation, and intensity. other studies contextualize euphemism detection in a specific use case. for instance, @xcite train models to detect hateful content or euphemistic hate speech. they employ word embeddings and network analysis, creating clusters of euphemisms by using eigenvector centralities as a ranking metric. furthermore, euphemism detection can also be used in crime detection. @xcite analyze jargon from cybercrime marketplaces to find patterns in phrases or code words commonly used in underground communications. however, these two methods use static word embeddings, which do not take into account the context. this may affect performance, as context is very important for euphemisms. in contrast, our method uses context-aware embeddings of transformer-based models. ## conclusion and future work we proposed eureka, a method for classifying euphemistic usage in a sentence. this is an ensemble model that uses ideas such as data cleaning, data augmentation, representations of potentially euphemistic terms (pets), and k-nearest-neighbor predictions. our eureka system achieves a score of 0.881 and ranks first on the public leaderboard for the shared task. in the future, we hope to extend our methods to dysphemisms or other figurative language instances. it is also interesting to consider a zeroshot setting for euphemism detection, where euphemisms during test time are unseen during training. figurative language generation, rather than detection, could also be a fruitful area to explore.