Ensemble-based Multilingual Euphemism Detection: a Behavior-Guided Approach (2024)

Abstract: This paper describes the system submitted by our team to the Multilingual Euphemism Detection Shared Task for the Fourth Workshop on Figurative Language Processing (FigLang 2024). We propose a novel model for multilingual euphemism detection, combining contextual and behavior-related features. The system classifies texts that potentially contain euphemistic terms with an ensemble classifier based on outputs from behavior-related fine-tuned models. Our results show that, for this kind of task, our model outperforms baselines and state-of-the-art euphemism detection methods. As for the leader-board, our classification model achieved a macro averaged F1 score of [anonymized], reaching the [anonymized] place.

## introduction euphemism, as defined by the oxford english dictionary, is the substitution of mild or indirect expressions for harsh or blunt ones when referring to unpleasant topics. the american heritage dictionary of the english language similarly defines euphemism as replacing harsh or offensive terms with milder, indirect ones. this paper explores the task of detecting euphemisms across multiple languages. euphemism is a linguistic strategy employed to soften the impact of direct or uncomfortable language, such as using 'collateral damage" instead of "war-related civilian deaths". euphemisms are commonly employed to maintain politeness, ease discomfort, or veil harsh realities in everyday communication. despite cultural differences in their usage, the universal need to discuss sensitive topics without causing offense suggests commonalities in how euphemisms are applied across languages and cultures. this study investigates how multilingual models can leverage these similarities in processing euphemisms. our work is part of a shared task for the fourth workshop on figurative language processing (figlang 2024) and focuses on the euphemism disambiguation task, in which potentially euphemistic termss (pets) are classified as euphemistic or not in a given context in four languages (chinese, english, spanish, and yorùbá). this set of languages helps to encompass a diverse range of linguistic and cultural backgrounds @xcite . our approach achieved the third-best score in the multilingual euphemism detection shared task. this paper describes our model foot_0 participating in the task. ## related work in this section, we explore related work about figurative language detection and euphemism detection in particular, utilization of behavior-related models for detecting specific types of content, and use of ensemble learning for combining different approaches for text classification. ## model architecture the model's architecture is presented in figure 1 and includes two main steps: fine-tuning for behavior-related downstream tasks and ensemble method for classification. first, we fine-tuned the multilingual transformerbased model (xlm-roberta @xcite ) for classifying contextual texts (without pets) and classifying pets separately. based on review of related work, we fine-tuned the same pre-trained language model for the several behavioral tasks: detection of sarcasm and irony @xcite , sexism, racism @xcite , and sentiment classification (passionate-nlp, 2021). after fine-tuning, we had 6 fine-tuned models with the same architecture, and tokenizers. second, our final model used the ensemble learning method for classification, which received logits from described models as features. during the developing step, we tested several ensemble models including: adaptive boosting, extra trees, gradient boosting, and random forest. finally, we used the best performing ensemble learning method to train model for detection euphemisms in four languages. ## experiment for the shared task, we made only multilingual experiments, i.e. training and developing datasets contain entities in all four presented languages. ## conclusion we have described a method for multilingual euphemism detection. this method is based on behaviour-related fine-tuning of transformer model for combining their logits in ensemble learning. experiments with four different languages demonstrate that our approach could reach high performance in the task.