# Irony Detection in Urdu Text: A Comparative Study Using Machine Learning Models and Large Language Models

arXiv: https://arxiv.org/abs/2510.22356 · HTML: https://mlsanity.com/paper/2510.22356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Irony Detection in a Multilingual Context](https://mlsanity.com/paper/2002.02427/) (2020)
- [Irony in Emojis: A Comparative Study of Human and LLM Interpretation](https://mlsanity.com/paper/2501.11241/) (2025)
- [Historical Ink: Exploring Large Language Models for Irony Detection in 19th-Century Spanish](https://mlsanity.com/paper/2503.22585/) (2025)
- [Fine-Tuning Large Language Models with QLoRA for Offensive Language Detection in Roman Urdu-English Code-Mixed Text](https://mlsanity.com/paper/2510.03683/) (2025)

## Abstract (arXiv)

Ironic identification is a challenging task in Natural Language Processing, particularly when dealing with languages that differ in syntax and cultural context. In this work, we aim to detect irony in Urdu by translating an English Ironic Corpus into the Urdu language. We evaluate ten state-of-the-art machine learning algorithms using GloVe and Word2Vec embeddings, and compare their performance with classical methods. Additionally, we fine-tune advanced transformer-based models, including BERT, RoBERTa, LLaMA 2 (7B), LLaMA 3 (8B), and Mistral, to assess the effectiveness of large-scale models in irony detection. Among machine learning models, Gradient Boosting achieved the best performance with an F1-score of 89.18%. Among transformer-based models, LLaMA 3 (8B) achieved the highest performance with an F1-score of 94.61%. These results demonstrate that combining transliteration techniques with modern NLP models enables robust irony detection in Urdu, a historically low-resource language.