FEED PET s: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms (2023)

Abstract: Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context. In this study, we expand on the task in two ways. First, we annotate PETs for vagueness, a linguistic property associated with euphemisms, and find that transformers are generally better at classifying vague PETs, suggesting linguistic differences in the data that impact performance. Second, we present novel euphemism corpora in three different languages: Yoruba, Spanish, and Mandarin Chinese. We perform euphemism disambiguation experiments in each language using multilingual transformer models mBERT and XLM-RoBERTa, establishing preliminary results from which to launch future work.

## introduction detecting and interpreting figurative language is a rapidly growing area in natural language processing (nlp) @xcite . unfortunately, little work has been done on euphemism processing. euphemisms are expressions that soften the message they convey. they are culture-specific and dynamic: they change over time. therefore, dictionary-based approaches are ineffective @xcite @xcite . euphemisms are often ambiguous: their figurative and non-figurative interpretation is often context-dependent; see to address this, the present study describes two experiments to expand upon the euphemism disambiguation task. in the first, we investigate a pragmatic property of euphemisms, vagueness, and use human annotations to distinguish between pets which are more vague (vague euphemistic terms, or vets) versus less vague. we then experiment with transformers' abilities to disambiguate examples containing vets versus non-vets, and find that performance is generally higher for vets. while we are unable to ascertain the exact reason for this discrepancy, we analyze the potential implications of the results and propose follow-up studies. in the second experiment, we create novel euphemism corpora for three other languages: yorùbá, (latin american and castilian) spanish, and mandarin chinese. similarly to the english data, examples are obtained using a seed list of pets, and include both euphemistic and non-euphemistic instances. we run initial experiments using multilingual transformer models mbert and xlm-roberta, testing their ability to classify them. the results establish preliminary baselines from which to launch future multilingual and cross-lingual work in euphemism processing. ## previous work in the past few years, there has been an interest in the nlp community in computational approaches to euphemisms. @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 used a weakly supervised algorithm for semantic lexicon induction @xcite to generate lists of near-synonym phrases for three sensitive topics (lying, stealing, and firing). the important product of this work is a gold-standard non-euphemistic euphemistic asked to choose between jobs and the environment, this summer, the budding talent agent was a majority -at least in our warped, between jobs and free to babysit pretty much first-past-the-post system -will pick jobs. while the performance of felt and riloff (2020)'s system is relatively low and the range of topics is very narrow, this work inspired other research on euphemism detection. thus, @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). the authors selected sentences matched by a list of keywords, created masked sentences (mask the keywords in the sentences) and applied the masked language model proposed in bert @xcite to filter out generic (uninformative) sentences and then generated expressions to fill in the blank. these expressions are ranked by relevance to the target topic. @xcite present the first corpus of potentially euphemistic terms (pets) along with example texts from the glowbe corpus. they also present a subcorpus of texts where these pets are not being used euphemistically. @xcite find that sentiment analysis on the euphemistic texts supports that pets generally decrease negative and offensive sentiment. they observe cases of disagreement in an annotation task, where humans are asked to label pets as euphemistic or not in a subset of our corpus text examples. the disagreement is attributed to a variety of potential reasons, including if the pet was a commonly accepted term (cat). this work is followed by @xcite who present a linguistically driven proof of concept for finding potentially euphemistic terms, or pets. acknowledging that pets tend to be commonly used expressions for a certain range of sensitive topics, they make use of distributional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentiment-based metrics. with regards to the euphemism disambiguation task, in which terms are classified as euphemistic or non-euphemistic, a variety of bert-based approaches featured in the 3rd workshop on figurative language processing have shown promising results. @xcite and @xcite both show that supplying the classifier with information about the term itself, such as embeddings and its literal (non-euphemistic) meaning, significantly boost performance, among other enhancements. in a zero-shot experiment, keh (2022) shows that bert can disambiguate pets unseen during training (albeit at a lower success rate), suggesting that some form of general knowledge is learned, though it is unclear what. ## vet experiments in this section, we discuss the concept of vague euphemistic terms (vets), and subsequent experiments. the linguistics literature often describes euphemisms as either 'more ambiguous' or 'vaguer' than the non-euphemistic expressions they substitute @xcite @xcite @xcite . we understand ambiguity as a countable property, when an expression can have a certain number of senses; whereas vagueness is not countable, a continuum of meaning or theoretically an infinite number of interpretations. however, we note that these qualities are on a "spectrum", and may not be equal for all euphemisms. see below for examples of some euphemisms which may be considered to be vets, and others, non-vets: vague: the funds will be used to @xmath0. (counter? peacefully or violently? kill? some other form of removing power?) non-euphemistic euphemistic pregnant woman woman in a certain condition aged care institution home, hostel, house, cottage, village, residence old age certain age false statements alternative facts war special military operation/campaign we have to change and do something we aren't used to we must reach beyond our fears being out of work being in transition a lack of consistent access to enough food for an active healthy life food insecurity prison correctional facility blind visually challenged, visually impaired vague: they were really starting to like each other, but did not know if they were ready @xmath1. (start dating? have sexual intercourse? begin or complete some other process?) nonvague: as part of their restructuring, the company @xmath2. nonvague: there is always gossip about @xmath3. additionally, @xcite ; @xcite observed that there are different kinds of potentially euphemistic terms (pets). one distinction they suggest is 'commonly accepted terms' (cats), which are so commonly used in a particular domain that they may have less pragmatic purpose (intention to be vague/neutral/indirect/etc.) than other euphemisms. some examples of pets which may be cats are "elderly", "same-sex", and "venereal disease". humans may disagree on whether these terms are euphemistic in context, since cats may be viewed as "default terms" rather than a deliberate attempt to be euphemistic. notably, since many of the pets under investigation are established expressions, we expect a fair amount to be non-vague; i.e., modern speakers of the language should precisely understand what the term means. the differences described above may be a factor in computational attempts to work with euphemisms; e.g., some examples may be harder to disambiguate. to investigate this, we assess transformers' performances on examples annotated to be "vague" versus those that are "non-vague". however, defining and determining the relative vagueness of an expression is not a trivial task. below, we describe our methodology for obtaining vagueness labels, experimental results and follow-up analyses. ## multilingual experiments euphemism disambiguation thus far has focused on american english. in this section, we describe euphemism disambiguation experiments run on multilingual data. for each of the different languages, native speakers and language experts created a list of pets, collected example texts for each pet, and annotated each text for whether the pet was being used euphemistically given the context. we then test the classification abilities of multilingual transformer models. the results are intended to show whether multilingual transformer models have the potential to disambiguate euphemisms in languages other than english, and establish preliminary baselines for the task. ## conclusion and future work this study presents an expansion of the euphemism disambiguation task. we describe our method for annotating vagueness, and show that this kind of pragmatic distinction may reveal interesting trends in bert's ability to perform nlu. namely, bert performs better for pets labeled as vets, which leads us to the potential result that bert may be associating the presence of "sensitive words" to euphemisms. corroborating this result and exploring additional properties of euphemisms are left for future work. the multilingual results show that bert models can already disambiguate euphemisms in multiple languages to some extent, and establish a baseline from which to improve results. while continuously expanding the multilingual corpora is a must, a number of modeling aspects can be investigated as well. for instance, error analyses can be run to reveal potential misclassification trends in each language, and data and modeling improvements that were shown to work for american english can be attempted on other languages. in general, such investigations may be used to suggest useful crosslingual features for pet disambiguation, and more broadly, universal properties of euphemisms.