# FEED PETs: Further Experimentation and Expansion on the Disambiguation of Potentially Euphemistic Terms

arXiv: https://arxiv.org/abs/2306.00217 · HTML: https://mlsanity.com/paper/2306.00217/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [FLUTE: Figurative Language Understanding through Textual Explanations](https://mlsanity.com/paper/2205.12404/) (2022)
- [Self-Supervised Euphemism Detection and Identification for Content Moderation](https://mlsanity.com/paper/2103.16808/) (2021)
- [yosm: A new yoruba sentiment corpus for movie reviews](https://mlsanity.com/paper/2204.09711/) (2022)
- [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)
- [Automatic Detection of Vague Words and Sentences in Privacy Policies](https://mlsanity.com/paper/1808.06219/) (2018)
- [A Report on the Euphemisms Detection Shared Task](https://mlsanity.com/paper/2211.13327/) (2022)
- [Combining Vagueness Detection with Deep Learning to Identify Fake News](https://mlsanity.com/paper/2110.14780/) (2021)
- [EUREKA: EUphemism Recognition Enhanced through Knn-based methods and Augmentation](https://mlsanity.com/paper/2210.12846/) (2022)
- [Exploring Euphemism Detection in Few-Shot and Zero-Shot Settings](https://mlsanity.com/paper/2210.12926/) (2022)
- [Detecting Euphemisms with Literal Descriptions and Visual Imagery](https://mlsanity.com/paper/2211.04576/) (2022)

## Built on by (most cited first, 3 of 3)

- [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)

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.