A Report on the Euphemisms Detection Shared Task (2022)

Abstract: This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022. Participants were invited to investigate the euphemism detection task: given input text, identify whether it contains a euphemism. The input data is a corpus of sentences containing potentially euphemistic terms (PETs) collected from the GloWbE corpus, and are human-annotated as containing either a euphemistic or literal usage of a PET. In this paper, we present the results and analyze the common themes, methods and findings of the participating teams.

## introduction euphemisms are mild or indirect expressions that are used in place of other ones when discussing potentially offensive or sensitive topics. their linguistic functions include (politeness, concealment, and neutralization of unappealing words/phrases). despite being an important element of language use, their figurative nature poses a challenge for natural language processing (nlp). there are numerous challenges to working with euphemisms. one is the phenomenon of the "euphemism treadmill", by which words/phrases gain or lose euphemistic meanings over time @xcite . another is that researchers may not agree on what euphemisms are. for example, @xcite ; @xcite treat code words as euphemisms, but our working definition does not. even when restricted to our working definition, however, annotators were found to sometimes disagree in the task of labeling example sentences as euphemistic @xcite . for all these reasons, the words/phrases in this shared task are referred to as potentially euphemistic terms (pets). the main challenge, which is the focus of this shared task, is the ambiguity of pets: the same words/phrases that may be euphemistic in one context may be literal in another. for example, 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 we propose the shared task on euphemism detection: given input text, identify whether it contains a euphemism; i.e., distinguish between euphemistic and literal usages of the same pets in different contexts. the data used is a corpus of texts containing pets, collected by @xcite , which contains parallel euphemistic and literal examples for a range of pets. 46 participants spanning 13 teams attempted the task, and we received 9 system descriptions. due to a lack of extensive research in this area, it is unclear how nlp techniques, such as language models (lms), will perform on euphemism detection. the purpose of this shared task is to (1) explore the ability of nlp techniques for this task and ( 2 ) investigate what methods could further improve upon their performance. ## related work there is not much work on automatic detection of euphemisms. the most directly related work is by @xcite , @xcite , @xcite , @xcite and @xcite . @xcite present the first effort to recognize euphemisms and dysphemisms (derogatory terms) using nlp. the authors use the term x-phemisms to refer to both. they first identify three sensitive topics (lying, stealing, and firing). they use a weakly supervised algorithm for semantic lexicon induction @xcite to generate lists of near-synonym phrases for each topic semi-automatically. @xcite experiment with two methods to classify phrases as euphemistic, dysphemistic, and neutral: 1) dictionary-based method addressing affect, connotation, intensity, arousal, and dominance; 2) contextual sentiment analysis to classify x-phemisms. the important product of this work is a gold-standard dataset of human x-phemism judgements. the important lesson here is that @xcite show that sentiment connotation and affective polarity are useful for identifying x-phemisms, but not sufficient. while the performance of felt and riloff (2020)'s system is relatively low and the range of topics is very narrow, this work certainly inspires further investigations. @xcite define two tasks: 1) euphemism detection (based on the input keywords, produce a list of candidate euphemisms) 2) euphemism identification (take the list of candidate euphemisms produced in (1) and output an interpretation). @xcite select sentences matched by a list of keywords, create masked sentences (mask the keywords in the sentences) and apply the masked language model proposed in bert @xcite to filter out generic (uninformative) sentences and then generate expressions to fill in the blank. these expressions are ranked by relevance to the target topic. euphemisms are also related to the language of politeness (e.g., @xcite ; rababah (2014)), which plays a role in applications involving dialogue and social interactions in different contexts. other shared tasks have proposed a similar classification task on other types of figurative language. ghosh et al. (2020 ) report on a sarcasm detection task run on conversation data from twitter and reddit, while madabushi et al. (2022) report on an idiom detection and embedding task. ## discussion and findings here, we describe some common findings of the submitted systems that may be useful for future work. ## conclusions and future work we present the results of "the shared task on euphemism detection for the third workshop on figurative language processing" and summarize the various systems submitted, as well as common findings. overall, we find that results are promising: even when dealing with the difficult issue of an especially polite and indirect form of figurative language, current nlp techniques such as transformers and augmentation seem to work quite well. teams explored a variety of intriguing methods to enhance the baseline performance of these models, some of which were even linguistically transparent. if one considers that labeling euphemisms is subject to human disagreement, the f1-scores achieved by the teams are even more compelling since they may be near, if not already at, the level of human agreement on the task. the results of this shared task establish a baseline for future work on euphemisms and figurative language in general. future work on this task could be expanding on the dataset to include more examples and a wider range of pets, testing further enhancements, and improving performance by ensembling various combinations of the best-performing improvements. future work for euphemism detection in general could be to expand from the disambiguation task; e.g. identifying where euphemisms are in a text, providing interpretations of a euphemism, or even euphemistic language generation.