# MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System

arXiv: https://arxiv.org/abs/2307.07135 · HTML: https://mlsanity.com/paper/2307.07135/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification](https://mlsanity.com/paper/2204.14244/) (2022)
- [Detecting Sarcasm in Multimodal Social Platforms](https://mlsanity.com/paper/1608.02289/) (2016)
- [Multimodal Fake News Detection via CLIP-Guided Learning](https://mlsanity.com/paper/2205.14304/) (2022)
- [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1610.08815/) (2016)
- [Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement](https://mlsanity.com/paper/2210.03501/) (2022)
- [Indonesian Social Media Sentiment Analysis With Sarcasm Detection](https://mlsanity.com/paper/1505.03085/) (2015)

## Built on by (most cited first, 30 of 31)

- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts](https://mlsanity.com/paper/2311.09580/) (2023)
- [SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding](https://mlsanity.com/paper/2408.11319/) (2024)
- [A Survey of Multimodal Sarcasm Detection](https://mlsanity.com/paper/2410.18882/) (2024)
- [Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey](https://mlsanity.com/paper/2406.08068/) (2024)
- [CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models](https://mlsanity.com/paper/2405.00390/) (2024)
- [Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing](https://mlsanity.com/paper/2509.15361/) (2025)
- [InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection](https://mlsanity.com/paper/2406.16464/) (2024)
- [Multi-View Incongruity Learning for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2412.00756/) (2024)
- [RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2412.13008/) (2024)
- [Seeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Models](https://mlsanity.com/paper/2503.12149/) (2025)
- [Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models](https://mlsanity.com/paper/2503.18681/) (2025)
- [IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection](https://mlsanity.com/paper/2505.16258/) (2025)
- [Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2510.11852/) (2025)
- [SatireDecoder: Visual Cascaded Decoupling for Enhancing Satirical Image Comprehension](https://mlsanity.com/paper/2512.00582/) (2025)
- [Are MLMs Trapped in the Visual Room?](https://mlsanity.com/paper/2505.23272/) (2025)
- [Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2506.19420/) (2025)
- [Evaluating Multimodal Large Language Models on Spoken Sarcasm Understanding](https://mlsanity.com/paper/2509.15476/) (2025)
- [CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark](https://mlsanity.com/paper/2604.16372/) (2026)
- [Adapter-state Sharing CLIP for Parameter-efficient Multimodal Sarcasm Detection](https://mlsanity.com/paper/2507.04508/) (2025)
- [MuSaG: A Multimodal German Sarcasm Dataset with Full-Modal Annotations](https://mlsanity.com/paper/2510.24178/) (2025)
- [GDCNet: Generative Discrepancy Comparison Network for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2601.20618/) (2026)
- [Small Cues, Big Consequences: Learning Pivotal Cues for Multimodal Meme Classification](https://mlsanity.com/paper/2609.26907/) (2026)
- [ViSP: A PPO-Driven Framework for Sarcasm Generation with Contrastive Learning](https://mlsanity.com/paper/2507.09482/) (2025)
- [Teaching Sarcasm: Few-Shot Multimodal Sarcasm Detection via Distillation to a Parameter-Efficient Student](https://mlsanity.com/paper/2510.25303/) (2025)
- [ProCrit: Self-Elicited Multi-Perspective Reasoning with Critic-Guided Revision for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2605.20867/) (2026)
- [Beyond the Literal: Decomposing Pragmatic Intent in Multimodal Meme Understanding](https://mlsanity.com/paper/2606.03604/) (2026)
- [HCIG: A Hierarchical Cross-Modal Incongruity Graph Network for Multimodal Sarcasm and Cyberbullying Detection](https://mlsanity.com/paper/2607.16076/) (2026)
- [MoCA: Implicit Social Context Analysis](https://mlsanity.com/paper/2608.05825/) (2026)
- [PragMatch: Separating Pragmatic Incongruity from Cross-Modal Mismatch in Large Vision-Language Models](https://mlsanity.com/paper/2608.09772/) (2026)

## Abstract (arXiv)

Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-modal sarcasm detection system: (1) There are some spurious cues in MMSD, leading to the model bias learning; (2) The negative samples in MMSD are not always reasonable. To solve the aforementioned issues, we introduce MMSD2.0, a correction dataset that fixes the shortcomings of MMSD, by removing the spurious cues and re-annotating the unreasonable samples. Meanwhile, we present a novel framework called multi-view CLIP that is capable of leveraging multi-grained cues from multiple perspectives (i.e., text, image, and text-image interaction view) for multi-modal sarcasm detection. Extensive experiments show that MMSD2.0 is a valuable benchmark for building reliable multi-modal sarcasm detection systems and multi-view CLIP can significantly outperform the previous best baselines.