# Irony Detection in a Multilingual Context

arXiv: https://arxiv.org/abs/2002.02427 · HTML: https://mlsanity.com/paper/2002.02427/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language](https://mlsanity.com/paper/2106.12488/) (2021)
- [Deception detection in text and its relation to the cultural dimension of individualism/collectivism](https://mlsanity.com/paper/2105.12530/) (2021)
- [Make Satire Boring Again: Reducing Stylistic Bias of Satirical Corpus by Utilizing Generative LLMs](https://mlsanity.com/paper/2412.09247/) (2024)
- [sarcasm detection and quantification in arabic tweets](https://mlsanity.com/paper/2108.01425/) (2021)
- [BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts](https://mlsanity.com/paper/2205.08868/) (2022)
- [Irony Detection in Urdu Text: A Comparative Study Using Machine Learning Models and Large Language Models](https://mlsanity.com/paper/2510.22356/) (2025)

## Abstract (arXiv)

This paper proposes the first multilingual (French, English and Arabic) and multicultural (Indo-European languages vs. less culturally close languages) irony detection system. We employ both feature-based models and neural architectures using monolingual word representation. We compare the performance of these systems with state-of-the-art systems to identify their capabilities. We show that these monolingual models trained separately on different languages using multilingual word representation or text-based features can open the door to irony detection in languages that lack of annotated data for irony.