# CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic

arXiv: https://arxiv.org/abs/2206.08415 · HTML: https://mlsanity.com/paper/2206.08415/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 16 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [Semi-Supervised Learning with Generative Adversarial Networks](https://mlsanity.com/paper/1606.01583/) (2016)
- [Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment Classification](https://mlsanity.com/paper/1908.11860/) (2019)
- [iSarcasm: A Dataset of Intended Sarcasm](https://mlsanity.com/paper/1911.03123/) (2019)
- [A Report on the 2020 Sarcasm Detection Shared Task](https://mlsanity.com/paper/2005.05814/) (2020)
- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019)
- [Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language](https://mlsanity.com/paper/2106.12488/) (2021)
- [Exploring Author Context for Detecting Intended vs Perceived Sarcasm](https://mlsanity.com/paper/1910.11932/) (2019)
- [BERT-based Multi-Task Model for Country and Province Level Modern Standard Arabic and Dialectal Arabic Identification](https://mlsanity.com/paper/2106.12495/) (2021)
- [Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach](https://mlsanity.com/paper/1905.06484/) (2019)

## Abstract (arXiv)

Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning. This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentiment Analysis, Opinion Mining, and Author Profiling. In this paper, we present our participating system to the intended sarcasm detection task in English and Arabic languages. Our systemfootnoteThe source code of our system is available at urlhttps://github.com/AbdelkaderMH/iSarcasmEval consists of three deep learning-based models leveraging two existing pre-trained language models for Arabic and English. We have participated in all sub-tasks. Our official submissions achieve the best performance on sub-task A for Arabic language and rank second in sub-task B. For sub-task C, our system is ranked 7th and 11th on Arabic and English datasets, respectively.