# Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language

arXiv: https://arxiv.org/abs/2106.12488 · HTML: https://mlsanity.com/paper/2106.12488/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [A review of sentiment analysis research in Arabic language](https://mlsanity.com/paper/2005.12240/) (2020)
- [Irony Detection in a Multilingual Context](https://mlsanity.com/paper/2002.02427/) (2020)

## Built on by (most cited first, 12 of 12)

- [Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation](https://mlsanity.com/paper/2306.16650/) (2023)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension](https://mlsanity.com/paper/2412.11906/) (2024)
- [Arabic Text Diacritization In The Age Of Transfer Learning: Token Classification Is All You Need](https://mlsanity.com/paper/2401.04848/) (2024)
- [SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm](https://mlsanity.com/paper/2301.02521/) (2023)
- [UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer](https://mlsanity.com/paper/2204.13515/) (2022)
- [Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification](https://mlsanity.com/paper/2205.07960/) (2022)
- [A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2502.03827/) (2025)
- [sarcasm detection and quantification in arabic tweets](https://mlsanity.com/paper/2108.01425/) (2021)
- [Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media](https://mlsanity.com/paper/2206.08407/) (2022)
- [Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST](https://mlsanity.com/paper/2109.14456/) (2021)
- [CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic](https://mlsanity.com/paper/2206.08415/) (2022)

## Abstract (arXiv)

The prominence of figurative language devices, such as sarcasm and irony, poses serious challenges for Arabic Sentiment Analysis (SA). While previous research works tackle SA and sarcasm detection separately, this paper introduces an end-to-end deep Multi-Task Learning (MTL) model, allowing knowledge interaction between the two tasks. Our MTL model's architecture consists of a Bidirectional Encoder Representation from Transformers (BERT) model, a multi-task attention interaction module, and two task classifiers. The overall obtained results show that our proposed model outperforms its single-task counterparts on both SA and sarcasm detection sub-tasks.