- The paper introduces Turkey’s first comprehensive PETs dataset with 6,074 annotated euphemistic expressions from diverse cultural contexts.
- The study employs transformer models, notably BERTurk and Electra, with Electra achieving an accuracy of 0.86 in euphemism detection.
- The research underscores the importance of language-specific training for enhancing NLP applications in content moderation and figurative language analysis.
Analyzing Turkish Euphemistic Terms: Development and Evaluation of the Turkish PETs Dataset
Euphemisms, defined as polite or indirect expressions substituted for harsh or unpleasant terms, pose a unique challenge for NLP. Despite their ubiquitous presence in human communication, euphemisms are relatively understudied in computational linguistics, especially outside of the English language. The paper "Turkish Delights: a Dataset on Turkish Euphemisms" seeks to bridge this gap by introducing the first dataset of potentially euphemistic terms (PETs) in Turkish and evaluating the performance of various transformer-based models on the task of euphemism detection.
Dataset Development
The researchers have notably focused on creating a comprehensive Turkish PET dataset, addressing the inherent challenge of euphemism variability and cultural specificity. The dataset contains 6,074 annotated examples, categorizing euphemistic expressions into ten distinct groups: bodily functions, death, employment/finances, illness, miscellaneous, physical/mental attributes, politics, sexual activity, substances, and social topics. A meticulous annotation process was employed, enlisting Turkish annotators with linguistic backgrounds to ensure accuracy and cultural relevance.
A unique aspect of this paper is the balanced subset derived from the main dataset, ensuring a more equitable distribution of euphemistic and non-euphemistic examples. It contains a total of 908 instances, with 521 euphemistic and 387 non-euphemistic examples. Metrics such as average sentence length and lexical density were used to assess the dataset, providing a robust foundation for subsequent model training and evaluation.
Experimental Setup
The paper explores the efficacy of several transformer-based models for euphemism detection in Turkish, specifically BERTurk, Electra, mBERT, and XLM-RoBERTa. These models were fine-tuned on the balanced dataset, employing standard hyperparameters for fine-tuning, such as a learning rate of 1e-5, batch size of 4, and early stopping with a patience of 5 epochs. The balanced dataset was split in an 80-10-10 ratio for training, validation, and testing, respectively.
The experimental results revealed that monolingual models, particularly those pre-trained on Turkish text like BERTurk and electra-base-turkish-cased-discriminator, outperformed multilingual models. Specifically, Electra exhibited the highest performance with an accuracy, precision, recall, and F1 score of 0.86. These results underscore the importance of language-specific training for nuanced tasks like euphemism detection, where cultural and linguistic subtleties play a critical role.
Practical and Theoretical Implications
From a practical standpoint, the successful development and application of these models could significantly enhance content moderation systems, social media monitoring, and sentiment analysis in Turkish. The ability to automatically detect euphemisms could aid platforms in identifying and mitigating potentially harmful content disguised under polite terms, thereby maintaining a respectful and safe online environment.
Theoretically, this research contributes to a broader understanding of how euphemisms function across different languages and cultures. The creation of the Turkish PETs dataset opens avenues for comparative studies and cross-linguistic analyses, ultimately enriching the field of figurative language processing.
Future Directions
Future research should focus on expanding the dataset to include a wider range of PETs and contexts, ensuring a more extensive representation of euphemistic expressions in Turkish. Additionally, exploring more sophisticated model architectures or training techniques might yield further improvements in euphemism detection accuracy. The application of explainability techniques could also provide deeper insights into the models' decision-making processes, elucidating which linguistic features are most indicative of euphemistic usage.
The potential for cross-lingual transfer learning remains a promising area for exploration. Assessing how models trained on the Turkish dataset perform on other low-resource languages could pave the way for the development of universal euphemism detection systems. Addressing the ambiguity of PETs, as highlighted by Gavidia et al. (2022), should also be a focal point, leveraging advanced disambiguation techniques to better distinguish between euphemistic and non-euphemistic usages.
Conclusion
This research marks a significant step forward in the computational paper of euphemisms, particularly in a low-resource language like Turkish. The Turkish PETs dataset and the performance evaluation of various transformer-based models offer valuable insights and practical tools for advancing NLP applications in figurative language understanding. By establishing a foundation for future investigations, this paper fosters a richer, more nuanced comprehension of euphemisms across diverse linguistic and cultural landscapes.