# When Semantic Overlap Is Not Enough: Cross-Lingual Euphemism Transfer Between Turkish and English

arXiv: https://arxiv.org/abs/2602.16957 · HTML: https://mlsanity.com/paper/2602.16957/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Self-Supervised Euphemism Detection and Identification for Content Moderation](https://mlsanity.com/paper/2103.16808/) (2021)
- [CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms](https://mlsanity.com/paper/2205.02728/) (2022)
- [Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms](https://mlsanity.com/paper/2205.10451/) (2022)
- [A Report on the Euphemisms Detection Shared Task](https://mlsanity.com/paper/2211.13327/) (2022)
- [A diachronic evaluation of gender asymmetry in euphemism](https://mlsanity.com/paper/2106.02083/) (2021)
- [MEDs for PETs: Multilingual Euphemism Disambiguation for Potentially Euphemistic Terms](https://mlsanity.com/paper/2401.14526/) (2024)
- [Turkish Delights: a Dataset on Turkish Euphemisms](https://mlsanity.com/paper/2407.13040/) (2024)
- [When Does Language Transfer Help? Sequential Fine-Tuning for Cross-Lingual Euphemism Detection](https://mlsanity.com/paper/2508.11831/) (2025)

## Abstract (arXiv)

Euphemisms substitute socially sensitive expressions, often softening or reframing meaning, and their reliance on cultural and pragmatic context complicates modeling across languages. In this study, we investigate how cross-lingual equivalence influences transfer in multilingual euphemism detection. We categorize Potentially Euphemistic Terms (PETs) in Turkish and English into Overlapping (OPETs) and Non-Overlapping (NOPETs) subsets based on their functional, pragmatic, and semantic alignment. Our findings reveal a transfer asymmetry: semantic overlap is insufficient to guarantee positive transfer, particularly in low-resource Turkish-to-English direction, where performance can degrade even for overlapping euphemisms, and in some cases, improve under NOPET-based training. Differences in label distribution help explain these counterintuitive results. Category-level analysis suggests that transfer may be influenced by domain-specific alignment, though evidence is limited by sparsity.