# A Report on the 2020 Sarcasm Detection Shared Task

arXiv: https://arxiv.org/abs/2005.05814 · HTML: https://mlsanity.com/paper/2005.05814/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 24 of 24)

- [Hate Speech and Counter Speech Detection: Conversational Context Does Matter](https://mlsanity.com/paper/2206.06423/) (2022)
- [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://mlsanity.com/paper/2109.03587/) (2021)
- [Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification](https://mlsanity.com/paper/2209.13017/) (2022)
- [The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://mlsanity.com/paper/2311.05552/) (2023)
- [Hate Cannot Drive out Hate: Forecasting Conversation Incivility following Replies to Hate Speech](https://mlsanity.com/paper/2312.04804/) (2023)
- [A Report on the Euphemisms Detection Shared Task](https://mlsanity.com/paper/2211.13327/) (2022)
- ["Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space](https://mlsanity.com/paper/2101.10952/) (2021)
- [On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMs](https://mlsanity.com/paper/2410.12600/) (2024)
- [A Survey on Automated Sarcasm Detection on Twitter](https://mlsanity.com/paper/2202.02516/) (2022)
- [ConFiguRe: Exploring Discourse-level Chinese Figures of Speech](https://mlsanity.com/paper/2209.07678/) (2022)
- [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)
- [KoCoSa: Korean Context-aware Sarcasm Detection Dataset](https://mlsanity.com/paper/2402.14428/) (2024)
- [Class Distillation with Mahalanobis Contrast: An Efficient Training Paradigm for Pragmatic Language Understanding Tasks](https://mlsanity.com/paper/2505.11829/) (2025)
- [SASICM A Multi-Task Benchmark For Subtext Recognition](https://mlsanity.com/paper/2106.06944/) (2021)
- [IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English](https://mlsanity.com/paper/2203.02244/) (2022)
- [Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation](https://mlsanity.com/paper/2203.04111/) (2022)
- [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)
- [TEDB System Description to a Shared Task on Euphemism Detection 2022](https://mlsanity.com/paper/2301.06602/) (2023)
- [Token-free Models for Sarcasm Detection](https://mlsanity.com/paper/2505.01006/) (2025)
- [Conditional Information Bottleneck for Multimodal Fusion: Overcoming Shortcut Learning in Sarcasm Detection](https://mlsanity.com/paper/2508.10644/) (2025)
- [CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models](https://mlsanity.com/paper/2603.09993/) (2026)
- [You Really Didn't Get That? Benchmarking Social Pragmatic Inference for Indirect and Playful Chinese Online Comments](https://mlsanity.com/paper/2609.04384/) (2026)
- [Social Bias in Chinese Large Language Models under Social Media Expressions](https://mlsanity.com/paper/2610.14879/) (2026)

## Abstract (arXiv)

Detecting sarcasm and verbal irony is critical for understanding people's actual sentiments and beliefs. Thus, the field of sarcasm analysis has become a popular research problem in natural language processing. As the community working on computational approaches for sarcasm detection is growing, it is imperative to conduct benchmarking studies to analyze the current state-of-the-art, facilitating progress in this area. We report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing (FigLang 2020) at ACL 2020.