# Sarcasm Detection using Hybrid Neural Network

arXiv: https://arxiv.org/abs/1908.07414 · HTML: https://mlsanity.com/paper/1908.07414/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2019.

## Built on by (most cited first, 23 of 23)

- [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://mlsanity.com/paper/2302.14233/) (2023)
- [News Category Dataset](https://mlsanity.com/paper/2209.11429/) (2022)
- [UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation](https://mlsanity.com/paper/2204.08198/) (2022)
- [Cyclical Curriculum Learning](https://mlsanity.com/paper/2202.05531/) (2022)
- [News Headlines Dataset For Sarcasm Detection](https://mlsanity.com/paper/2212.06035/) (2022)
- [Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth](https://mlsanity.com/paper/2509.03867/) (2025)
- [Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models](https://mlsanity.com/paper/2503.18681/) (2025)
- [Does Commonsense help in detecting Sarcasm?](https://mlsanity.com/paper/2109.08588/) (2021)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [Retrieval-Augmented Meta Learning for Low-Resource Text Classification](https://mlsanity.com/paper/2309.04979/) (2023)
- [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://mlsanity.com/paper/2412.12761/) (2024)
- [Leveraging Multi-domain, Heterogeneous Data using Deep Multitask Learning for Hate Speech Detection](https://mlsanity.com/paper/2103.12412/) (2021)
- [Development and Comparison of Scoring Functions in Curriculum Learning](https://mlsanity.com/paper/2202.06823/) (2022)
- [A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detection](https://mlsanity.com/paper/2503.11838/) (2025)
- [Class Distillation with Mahalanobis Contrast: An Efficient Training Paradigm for Pragmatic Language Understanding Tasks](https://mlsanity.com/paper/2505.11829/) (2025)
- [Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media](https://mlsanity.com/paper/2101.05875/) (2021)
- [Compressibility of Distributed Document Representations](https://mlsanity.com/paper/2110.07595/) (2021)
- [A Robust Optimization Method for Label Noisy Datasets Based on Adaptive Threshold: Adaptive-k](https://mlsanity.com/paper/2203.14165/) (2022)
- [Finetuning for Sarcasm Detection with a Pruned Dataset](https://mlsanity.com/paper/2212.12213/) (2022)
- [Investigating Semi-Supervised Learning Algorithms in Text Datasets](https://mlsanity.com/paper/2401.01843/) (2024)
- [SaRoHead: Detecting Satire in a Multi-Domain Romanian News Headline Dataset](https://mlsanity.com/paper/2504.07612/) (2025)
- [MuSaRoNews: A Multidomain, Multimodal Satire Dataset from Romanian News Articles](https://mlsanity.com/paper/2504.07826/) (2025)
- [J-Miner: Recovering the Decision Logic of Fine-Tuned LLM Classifiers as Compact Rules](https://mlsanity.com/paper/2608.17063/) (2026)

## Abstract (arXiv)

Sarcasm Detection has enjoyed great interest from the research community, however the task of predicting sarcasm in a text remains an elusive problem for machines. Past studies mostly make use of twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. To overcome these shortcoming, we introduce a new dataset which contains news headlines from a sarcastic news website and a real news website. Next, we propose a hybrid Neural Network architecture with attention mechanism which provides insights about what actually makes sentences sarcastic. Through experiments, we show that the proposed model improves upon the baseline by ~ 5% in terms of classification accuracy.