A Prompt Based Approach for Euphemism Detection (2022)

Abstract: Euphemism is an indirect way to express sensitive topics. People can comfortably communicate with each other about sensitive topics or taboos by using euphemisms. The Euphemism Detection Shared Task in the Third Workshop on Figurative Language Processing co-located with EMNLP 2022 provided a euphemism detection dataset that was divided into the train set and the test set. We made euphemism detection experiments by prompt tuning pre-trained language models on the dataset. We used RoBERTa as the pre-trained language model and created suitable templates and verbalizers for the euphemism detection task. Our approach achieved the third-best score in the euphemism detection shared task. This paper describes our model participating in the task.

## introduction euphemism is a common linguistic phenomenon that is indispensable in everyday communication. in daily communication, people often encounter some sensitive topics or taboos that are inconvenient to express directly, such as death, age, income, etc., and need to borrow some synonymous sentences to express them tactfully or use words that are related to the original meaning. the social function of euphemism enables people to express their thoughts more freely and to communicate easily and happily. euphemism detection is a classification task in natural language processing and it can be divided into phrase-level euphemism detection and sentence-level euphemism detection. in english, euphemisms consist of one or more words, for example, indigent is a euphemistic word that refers to a poor or needy person; deceased means a person who has died; a sex worker means a prostitute euphemistically. phrase-level euphemism detection refers to the task of identifying euphemistic phrases in a sentence or paragraph. a model needs to recognize the euphemistic phrases in the target sentences. phrase-level euphemism detection can be modeled as a token classification task. sentence-level euphemism detection refers to the task of determining whether a sentence or a paragraph contains a euphemism. for example, the sentence "give me a moment, i just need to run to the powder room." contains a euphemistic phrase "powder room" which means toilet, so the sentence could be classified as euphemistic. a model needs to determine whether target sentences contain any euphemistic phrases. sentence-level euphemism detection can be modeled as a sequence classification task. the euphemism detection shared task in the third workshop on figurative language processing co-located with emnlp 2022 is a sentence-level euphemism detection task, which provides a sentence-level euphemism detection dataset based on glowbe @xcite dataset. the euphemism detection dataset consists of a total of 1965 sentences, among them 1382 sentences containing euphemistic terms and 777 sentences that don't contain euphemistic terms. the dataset is divided into a train set and a test set, the train set contains 1572 sentences with target labels, and the test set contains 393 sentences without target labels. the task requires participants to develop a model which can determine whether the target sentence contains any euphemistic terms. a model can train on the train set and predict the labels of the test set. prompt learning adds additional prompt information to the original input sentence and transforms it into cloze forms to make the pretrained language model better understand and ## related works in recent years, people have made some explorations on euphemism detection, constructed some euphemism detection datasets, and made some efforts to recognize euphemistic phrases and sentences. in this section, we will introduce these works by phrase level and sentence level respectively. ## system overview prompt-tuning is a method of using pre-trained language models, which requires transforming downstream tasks into cloze forms so that the pretraining process of pre-trained language models and the training process of downstream tasks can be unified. the prompt-tuning method can unlock the potential of pre-trained language models and it is a better few-shot learning method compared with the fine-tuning method. considering these advantages of prompt-tuning, we conducted our euphemism detection experiments using it. ## experiments in this section, we will introduce the specifics of the experiments, including dataset statistics, 4 coding framework, hyperparameters of experiments, results of experiments and analysis, etc. ## conclusions in this paper, we described the euphemism detection model based on prompt learning and the details of euphemism detection experiments based on this model. we selected roberta as our pretrained language model, selected the ptr template and ptr verbalizer, made some reasonable changes to the ptr verbalizer, implemented euphemism detection experiments using this model, and achieved better results on the euphemism detection dataset.