IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection

• URL: http://arxiv.org/abs/2505.16258v1
• Date: Thu, 22 May 2025 05:49:01 GMT
• Title: IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection
• Authors: Aashish Anantha Ramakrishnan, Aadarsh Anantha Ramakrishnan, Dongwon Lee,
• Abstract summary: We present IRONIC, an in-context learning framework that leverages Multi-modal Coherence Relations to analyze referential, analogical and pragmatic image-text linkages.<n>Our experiments show that IRONIC achieves state-of-the-art performance on zero-shot Multi-modal Sarcasm Detection.
• Score: 5.246809683975664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interpreting figurative language such as sarcasm across multi-modal inputs presents unique challenges, often requiring task-specific fine-tuning and extensive reasoning steps. However, current Chain-of-Thought approaches do not efficiently leverage the same cognitive processes that enable humans to identify sarcasm. We present IRONIC, an in-context learning framework that leverages Multi-modal Coherence Relations to analyze referential, analogical and pragmatic image-text linkages. Our experiments show that IRONIC achieves state-of-the-art performance on zero-shot Multi-modal Sarcasm Detection across different baselines. This demonstrates the need for incorporating linguistic and cognitive insights into the design of multi-modal reasoning strategies. Our code is available at: https://github.com/aashish2000/IRONIC

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