A Unified Generative Framework for Bilingual Euphemism Detection and Identification (2024)

Abstract: Various euphemisms are emerging in social networks, attracting widespread attention from the natural language processing community. However, existing euphemism datasets are only domain-specific or language-specific. In addition, existing approaches to the study of euphemisms are one-sided. Either only the euphemism detection task or only the euphemism identification task is accomplished, lacking a unified framework. To this end, we construct a large-scale Bilingual Multi-category dataset of Euphemisms named BME, which covers a total of 12 categories for two languages, English and Chinese. Then, we first propose a unified generative model to Jointly conduct the tasks of bilingual Euphemism Detection and Identification named JointEDI. By comparing with LLMs and human evaluation, we demonstrate the effectiveness of the proposed JointEDI and the feasibility of unifying euphemism detection and euphemism identification tasks. Moreover, the BME dataset also provides a new reference standard for euphemism detection and euphemism identification.

## introduction euphemisms are forms of language that express ideas or convey information through the use of indirect or cryptic language. the original intention of using euphemisms is to avoid direct, blunt, or potentially offensive expressions @xcite . however, to avoid explicitly expressing unfriendly views or statements, some users choose to use euphemisms to cover up discriminatory, insulting, or unfair remarks @xcite . as shown in figure 1 , lawbreakers use euphemisms (eg: "weed" means "wild grass" in literal english, but "drugs" * equal contribution. ## task definition unlike previous tasks focusing solely on euphemism detection or identification, our primary goal is to integrate both aspects into a unified modeling framework. our core objective is to propose a unified framework capable of automatically detecting euphemisms in text and identifying them into the correct categories. it is known that a sentence containing a euphemism, @xmath0, ..., <pet>, ..., w i , ..., w m ] (where pet is known to be a potential euphemism term). our goal is to determine whether pet represents a euphemism in a sentence and, if pet is a euphemism, identify the category to which pet belongs. as shown in figure 1 , here are the inputs and outputs for the euphemism detection and identification task: input: "my little bro smokes @xmath1." output: "target: euphemism label: 1, class label: substances." the euphemism label of the model's output is "1", which indicates that "weed" represents a euphemism in the sentence, and the class label of the model's output is "substances", which indicates that "weed" is categorized into "substances". ## conclusion in this paper, we construct a bilingual multicategory euphemism dataset named bme, which contains two languages, english and chinese. the bme dataset covers more than a dozen categories, which provides a new benchmark for research on euphemism detection and euphemism identification tasks. meanwhile, we also propose a novel generative approach to unify the euphemism detection task and the euphemism identification task, which proves the effectiveness of our proposed jointedi and the difficulty of this task by comparing it with llms and human evaluation. new insights and reference standards are provided for the research on the euphemism task.