mlsanityData to 14 Oct 2026

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

arXiv 2307.07135 ↗First posted 14 Jul 2023cs.CLcited by 31

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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.

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

13 of 29 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  5. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  6. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,127
  7. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
  8. 2022CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classificationcited by 65
  9. 2016Detecting Sarcasm in Multimodal Social Platformscited by 44
  10. 2022Multimodal Fake News Detection via CLIP-Guided Learningcited by 42
  11. 2016A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networkscited by 33
  12. 2022Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancementcited by 27
1 more
  1. 2015Indonesian Social Media Sentiment Analysis With Sarcasm Detectioncited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 8; 2025: 13; 2026: 9.

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  1. 2024Large Language Models Meet NLP: A Surveycited by 103
  2. 2023MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Expertscited by 21
  3. 2024SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understandingcited by 20
  4. 2024A Survey of Multimodal Sarcasm Detectioncited by 19
  5. 2024Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Surveycited by 16
  6. 2024CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Modelscited by 8
  7. 2025Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routingcited by 5
  8. 2024InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detectioncited by 4
  9. 2024Multi-View Incongruity Learning for Multimodal Sarcasm Detectioncited by 4
  10. 2024RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detectioncited by 4
  11. 2025Seeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Modelscited by 4
  12. 2025Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Modelscited by 3
19 more
  1. 2025IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detectioncited by 3
  2. 2025Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detectioncited by 3
  3. 2025SatireDecoder: Visual Cascaded Decoupling for Enhancing Satirical Image Comprehensioncited by 3
  4. 2025Are MLMs Trapped in the Visual Room?cited by 2
  5. 2025Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detectioncited by 2
  6. 2025Evaluating Multimodal Large Language Models on Spoken Sarcasm Understandingcited by 2
  7. 2026CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmarkcited by 2
  8. 2025Adapter-state Sharing CLIP for Parameter-efficient Multimodal Sarcasm Detectioncited by 1
  9. 2025MuSaG: A Multimodal German Sarcasm Dataset with Full-Modal Annotationscited by 1
  10. 2026GDCNet: Generative Discrepancy Comparison Network for Multimodal Sarcasm Detectioncited by 1
  11. 2026Small Cues, Big Consequences: Learning Pivotal Cues for Multimodal Meme Classificationcited by 1
  12. 2025ViSP: A PPO-Driven Framework for Sarcasm Generation with Contrastive Learningcited by 0
  13. 2025Teaching Sarcasm: Few-Shot Multimodal Sarcasm Detection via Distillation to a Parameter-Efficient Studentcited by 0
  14. 2026ProCrit: Self-Elicited Multi-Perspective Reasoning with Critic-Guided Revision for Multimodal Sarcasm Detectioncited by 0
  15. 2026Beyond the Literal: Decomposing Pragmatic Intent in Multimodal Meme Understandingcited by 0
  16. 2026HCIG: A Hierarchical Cross-Modal Incongruity Graph Network for Multimodal Sarcasm and Cyberbullying Detectioncited by 0
  17. 2026MoCA: Implicit Social Context Analysiscited by 0
  18. 2026PragMatch: Separating Pragmatic Incongruity from Cross-Modal Mismatch in Large Vision-Language Modelscited by 0
  19. 2026Dynamic Gated Cross-Modal Fusion with Sarcastic-aware Contrastive Regularization for Multimodal Sarcasm Detectioncited by 0