Report on the Multilingual Euphemism Detection Task (2024)

Abstract: This paper presents the Multilingual Euphemism Detection Shared Task for the Fourth Workshop on Figurative Language Processing (FigLang 2024) held in conjunction with NAACL 2024. Participants were invited to attempt the euphemism detection task on four different languages (American English, global Spanish, Yorùbá, and Mandarin Chinese): given input text containing a potentially euphemistic term (PET), determine if its use is euphemistic or not. We present the expanded datasets used for the shared task, summarize each team’s methods and findings, and analyze potential implications for future research.

## introduction euphemisms are a linguistic device used to soften or neutralize language that may otherwise be harsh or awkward to state directly (e.g., "between jobs" instead of "unemployed","late" instead of "dead", "collateral damage" instead of "war-related civilian deaths"). by acting as alternative words or phrases, euphemisms are used in everyday language to maintain politeness, mitigate discomfort, or conceal the truth. while they are culturally-dependent, the need to discuss sensitive topics in a non-offensive way is universal, suggesting similarities in the way euphemisms are used across languages and cultures. terms which may be used euphemistically sometimes require context to determine a euphemistic usage: asked to choose between jobs and the environment, a majority -at least in our warped, first-past-thepost system -will pick jobs. @xcite this summer, the budding talent agent was between jobs and free to babysit pretty much any time. @xcite in this shared task, participants were invited to develop approaches and models to disambiguate texts (in multiple languages) as either euphemistic or not. the previous iteration of this task resulted in numerous insights from participating teams, but featured only an english dataset @xcite . by providing a multilingual iteration, we hoped to extend these findings to other languages and employ transfer learning to uncover possible crosslingual patterns @xcite . this paper is structured as follows: section 2 describes related work, section 3 describes the additional data collected for the competition foot_0 and the task setting, section 4 summarizes the participants' methods and results, and section 5 analyzes common findings and the future directions they suggest. ## participants and results in all, there were 3 teams that participated in the task and also submitted descriptions of their systems. a summary of their performances are in ## conclusion we present the results of the multilingual euphemism detection shared task. participants' systems scored well above the baselines, but well below their reported validation metrics. taken together, these results invite further work into using llms, ensembling/related tasks, and efficient models, which showed proficiency across languages, but leave much room for improvement. from a synthesis of the teams' findings, we also suggest that future work explore the impact of pets, model behavior beyond performance metrics, and connections with related linguistic tasks.