T urkish Delights: a Dataset on T urkish Euphemisms (2024)

Abstract: Euphemisms are a form of figurative language relatively understudied in natural language processing. This research extends the current computational work on potentially euphemistic terms (PETs) to Turkish. We introduce the Turkish PET dataset, the first available of its kind in the field. By creating a list of euphemisms in Turkish, collecting example contexts, and annotating them, we provide both euphemistic and non-euphemistic examples of PETs in Turkish. We describe the dataset and methodologies, and also experiment with transformer-based models on Turkish euphemism detection by using our dataset for binary classification. We compare performances across models using F1, accuracy, and precision as evaluation metrics.

## introduction euphemisms are polite or indirect words or expressions used in substitution of unpleasant or more offensive ones. they can be used to show kindness while discussing sensitive or taboo topics @xcite such as saying between jobs instead of unemployed, or as a way to make unpleasant or unappealing things sound less harsh @xcite , such as saying passed away, instead of died. similar to the word died in english, turkish makes use of many substitutions for the word öl-mek/öldü (to die/died), which is considered unpleasant. the substitutions for this word could be given as vefat etmek (to pass away), öbür dünyaya göçmek (to migrate to the other world), hakkın rahmetine kavuşmak (to go to kingdom come). euphemisms can be used to conceal the truth @xcite ; for instance, if one were to use the expression enhanced interrogation techniques, one would mean torture @xcite . furthermore, humans may not agree on what a euphemism is @xcite . there are various challenges regarding euphemisms. for instance, in some cases, words or expressions might develop or lose euphemistic meanings in time @xcite . due to the aforementioned reasons, the words and phrases in this research will be referred to as potentially euphemistic terms (pets) @xcite . euphemisms pose a challenge to natural language processing (nlp) due to this figurative behavior as they might also have a non-euphemistic interpretation in certain contexts. for example, while the turkish pet mercimegi fırına vermek means to put the lentil in the oven literally, it could mean to have sex/to get someone pregnant euphemistically. in the following sentence, this pet used literally: "günümüzde @xmath0, hem de fırında makarna yemek..." which can be translated as "nowadays, it's easier @xmath1. . . " however, it was used euphemistically in the following sentence: "gel gör ki kasabanın yegane doktoru ile pişiren bu kadın, zaman @xmath2, it turns out that this woman, who is having an affair with the town's only doctor, @xmath3. conducting a euphemism detection task in turkish has several challenges to overcome. firstly, to the best of our knowledge, there are no available datasets for automatic euphemism detection task in turkish. academic research, published books, articles, and other resources on this topic are very limited, making the collection of pets difficult. in this research, we aim to identify pets in turkish and create a dataset of turkish pets with the help of native turkish annotators who have a linguistics background. we fine-tune language models (lms) such as berturk @xcite and electra @xcite and large language models (llms), such as xlm-roberta (ai, 2019; @xcite and mbert @xcite for euphemism detection in turkish. therefore, the significant contributions of this paper are as follows: additionally, through extending euphemism detection task to a new language, we contribute to a better understanding of how euphemisms are utilized and interpreted across different linguistic and cultural contexts. ## turkish language agglutinative languages, such as turkish, form words by adding multiple affixes to a stem, with each affix representing a distinct morphological feature @xcite . this morphological productivity creates a vast number of possible word forms, making it difficult to develop comprehensive dictionaries or rule-based systems for tasks like euphemism detection. for instance, the pet hayata gözlerini yummak (to close one's eyes to life) can be formed as yum-du, yum-muş, yum-dugunda" and many other variations. see similar to euphemisms in other languages, the meaning of words and expressions are context dependent in turkish. while one word can be used euphemistically in one sentence, it might not have euphemistic meaning in another. for instance, the pet engelli might be used euphemistically to indicate that the person is disabled, but it might also have its non-euphemistic meaning of blocked. moreover, turkish is considered to be a lowresource language because of the limited availability of annotated datasets. it was also stated by various researchers that collecting data from various sources and labeling them was a challenging process @xcite . since there was no available dataset that contained euphemisms in turkish with examples, it was necessary for us to build a dataset and get it annotated by native turkish annotators. ## automatic euphemism detection euphemism detection can be viewed as a classification task in which an input text is classified as containing a euphemism or not. while this can be theoretically done at at the phrase-level or sentence-level euphemism detection, previous work has focused on classifying examples containing specific multi-word expressions, which may or may not be used euphemistically depending on the context @xcite . a number of approaches have performed decently at the task using language models such as transformers, improving upon baselines using various techniques. for example, @xcite use an ensemble of models each utilizing a combination of data and contextual augmentations to improve performance by 5 macro-f1 points. @xcite achieve similar improvements by incorporating non-euphemistic meanings and image embeddings associated with pets. @xcite propose a prompt-based approach for euphemism detection utilizing the language model roberta, achieving an f1 score of 85.2%, demonstrating the effectiveness of prompt-based learning. similar to our initial dataset, which contained more than 6,000 examples, the dataset they used was imbalanced and had more euphemistic examples than non-euphemistic. they noted the model's superior performance on euphemistic sentences compared to non-euphemistic ones due to this imbalance. given the nuanced nature of these expressions in the turkish language and the lack of previous work on figurative language processing in turkish, this study aims to investigate how well different language models identify and categorize pets in turkish. we fine-tuned two large multilingual models, xlm-roberta and mbert, along with lan- ## data collection and annotation 4.1 data collection to find pets in turkish, we analyzed the pets in other languages described in previous work @xcite , such as american english, mandarin chinese, yorùbá, and a mix of spanish dialects to see whether there were overlapping words or expressions used euphemistically (see through reviewing published articles and papers related to euphemisms in turkish, such as those by @xcite ; @xcite ; çabuk (2015), we expanded our list of pets. another method we used to collect pets was by posting polls on social media. initially, we explained the concept of "pets" and provided examples. we then utilized social media to share these polls, where turkish native speakers could share their ideas for new pets. as a result, our turkish pets list now comprises a total of 122 entries. we also included detailed information for each pet, such as euphemistic category (e.g. bodily functions), meaning, non-euphemistic meaning, literal translation, and the source it was from. the list is categorized into 10 groups with varying frequencies, which can be seen in once the pets list was finalized, we utilized a turkish corpus known as the ts corpus project @xcite . we selected ts corpus v2 and ts timeline corpus. ts corpus v2 drew from the boun web corpus and included 491,360,398 tokens and 4,950,407 word types. ts timeline corpus contained more than 700 million tokens and over 2.2 million news and articles. to search for texts containing pets for binary classification purposes, we utilized regular expressions, accounting for the agglutinative nature of the turkish language. this approach allowed us to capture various word forms effectively. for instance, for the pet hamileligi sonlandırmak (to terminate pregnancy), we designed a regular expression to detect all variations of hamile-lik (pregnancy), hamileligini (her pregnancy), hamile-ligimi (my pregnancy), sonlan-dırdı (terminated/has terminated), sonlan-dıracakmış (i heard that she will terminate), sonlan-dıramadı (she could not terminate), etc., r"(hamileli\w+ sonlan\w+)". as a result, we successfully captured variations of each pet were successfully captured. these captured pets were extracted and highlighted within their sentences using brackets, as shown: "duyduguma göre @xmath4." (i heard that her/his friend @xmath5.) additionally, we included preceding and succeeding sentence(s), if available, to form the entire example context for that pet. these contexts usually consisted of four sentences at most. not all pets on the initial list were found in the corpus; of the 122, only 58 were found and have at least one example. these examples were then compiled for the annotation phase. ## conclusion and future work in this study, we created a turkish pets dataset from scratch and through utilizing the dataset, we investigated the effectiveness of various language models in identifying and categorizing euphemisms in turkish. our findings indicate that models trained on multilingual data, particularly xlm-roberta, generally outperform monolingual models, suggesting the benefits of cross-lingual transfer learning in capturing euphemistic nuances. however, for the turkish language specifically, models trained on turkish text data, such as bert-baseturkish-cased and electra-base-turkish-cased future research could investigate the impact of model size, architecture, and training data on euphemism detection performance. additionally, exploring the use of explainability techniques could provide valuable insights into the decision-making processes of these models to better comprehend the specific linguistic features they rely on for euphemism detection. experimenting with different model architectures or training techniques might also further improve the performance of euphemism detection systems in turkish. additionally, expanding the dataset to include a wider range of euphemisms and exploring their application in downstream tasks like sentiment analysis and content moderation could be useful for future work. it is important to acknowledge that the results are based on a limited dataset and may not generalize to all types of euphemisms in turkish. future work could involve testing the models on a larger and more diverse dataset to confirm these findings. lastly, exploring the cross-lingual transferability of euphemism detection models trained on turkish data to other languages, similar to the work done in @xcite would provide valuable insights. this could involve fine-tuning multilingual models on turkish euphemisms and evaluating their performance on other languages. as highlighted in @xcite , the ambiguity of potentially euphemistic terms (pets) is a major challenge; therefore, future work could focus on developing methods to disambiguate pets and distinguish between their euphemistic and noneuphemistic usages more effectively.