IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection
IRONIC framework achieves state-of-the-art zero-shot sarcasm detection using multi-modal coherence relations.
Key Findings
Methodology
The IRONIC framework leverages multi-modal Coherence Relations (CR) for reasoning, analyzing image-text incongruencies. Key components include Relation Extraction and Interpretative Reasoning. Relation Extraction covers referential, analogical, and pragmatic reasoning, supporting comprehensive analysis of social media image-text relations. Interpretative Reasoning integrates image-text pairs, predicted CRs, and generated rationales to distinguish multi-modal sarcasm.
Key Results
- On the MMSD2.0 dataset, IRONIC improved zero-shot accuracy by 3.23%, reaching 76.59%.
- On the RedEval dataset, IRONIC combined with GPT-4o achieved an accuracy of 84.06%, surpassing all existing methods.
- Experiments show IRONIC outperforms existing multi-step reasoning strategies in multi-modal sarcasm detection.
Significance
This study highlights the importance of incorporating linguistic and cognitive insights into the design of multi-modal reasoning chains. The IRONIC framework achieves state-of-the-art performance in zero-shot multi-modal sarcasm detection, demonstrating that coherence relation reasoning capabilities are crucial in multi-modal tasks. This opens new research directions for applications like social media analysis, sentiment analysis, and human-computer interaction.
Technical Contribution
The IRONIC framework provides a new reasoning pathway by utilizing multi-modal coherence relations, surpassing traditional logical chain reasoning methods. Its innovation lies in combining pragmatic and cognitive structures, enabling superior performance in zero-shot settings and offering new engineering possibilities.
Novelty
IRONIC is the first framework to use multi-modal coherence relations for sarcasm detection. Compared to traditional logical chain reasoning methods, IRONIC achieves more efficient multi-modal sarcasm recognition by analyzing image-text incongruencies.
Limitations
- The current experiments are limited to a few top-performing proprietary and open-source models, not covering a broader range of models.
- Task-specific fine-tuning was not conducted, which may affect the accuracy of CR prediction and sarcasm recognition.
Future Work
Future work will expand the study to cover more models and explore multi-task fine-tuning strategies to improve both CR prediction and sarcasm recognition performance. Additionally, human evaluation will be conducted to analyze rationale quality and identify reasoning gaps.
AI Executive Summary
Multi-modal sarcasm detection is a complex task where traditional methods struggle with image-text incongruencies. Existing logical chain reasoning approaches fail to leverage the cognitive processes humans use to identify sarcasm.
The IRONIC framework introduces a new reasoning pathway by leveraging multi-modal coherence relations. Its key components include Relation Extraction and Interpretative Reasoning, effectively analyzing image-text incongruencies.
Experimental results show that IRONIC achieves state-of-the-art performance on the MMSD2.0 and RedEval datasets, demonstrating the crucial role of coherence relation reasoning capabilities in multi-modal tasks. This study opens new research directions for applications like social media analysis, sentiment analysis, and human-computer interaction.
Deep Analysis
Background
Multi-modal sarcasm detection is crucial in social media and sentiment analysis. Traditional supervised learning methods struggle with image-text incongruencies. Recent research focuses on the potential of Multi-modal Large Language Models (MLLMs), which excel in zero-shot settings, generalizing to new tasks without explicit training data.
Core Problem
The complexity of multi-modal sarcasm detection lies in its combination of semantic, pragmatic, and analogical cues, which are not always aligned with the logical and sequential nature of traditional reasoning strategies. This leads to models over-relying on uni-modal features, failing to discern sarcasm from other figurative language forms.
Innovation
The IRONIC framework introduces a new reasoning pathway by leveraging multi-modal coherence relations. Its innovation lies in combining pragmatic and cognitive structures, enabling superior performance in zero-shot settings. Compared to traditional logical chain reasoning methods, IRONIC achieves more efficient multi-modal sarcasm recognition by analyzing image-text incongruencies.
Methodology
- �� Relation Extraction: Utilizes CRs to analyze referential, analogical, and pragmatic reasoning, supporting comprehensive analysis of social media image-text relations.
- �� Interpretative Reasoning: Integrates image-text pairs, predicted CRs, and generated rationales to distinguish multi-modal sarcasm.
Experiments
Experiments were conducted on the MMSD2.0 and RedEval datasets to evaluate IRONIC's performance in zero-shot multi-modal sarcasm detection. MMSD2.0 is based on Twitter, re-annotated to remove unreasonable labels. RedEval evaluates model generalization across different domains.
Results
On the MMSD2.0 dataset, IRONIC improved zero-shot accuracy by 3.23%, reaching 76.59%. On the RedEval dataset, IRONIC combined with GPT-4o achieved an accuracy of 84.06%, surpassing all existing methods.
Applications
The IRONIC framework has significant applications in social media analysis, sentiment analysis, and human-computer interaction. It effectively analyzes image-text incongruencies, improving the accuracy of multi-modal sarcasm detection.
Limitations & Outlook
The current experiments are limited to a few top-performing proprietary and open-source models, not covering a broader range of models. Task-specific fine-tuning was not conducted, which may affect the accuracy of CR prediction and sarcasm recognition.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking. You have some ingredients (images) and a recipe (text). Normally, you'd follow the recipe to decide how to use the ingredients. But sometimes, the recipe and ingredients don't match, like the recipe calls for chicken, but you only have fish. You need to get creative and find a new way to combine them, just like the IRONIC framework does in multi-modal sarcasm detection. It analyzes the relationship between images and text to find inconsistencies and identify sarcasm.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game with lots of pictures and words. Usually, you need to understand the pictures based on the words. But sometimes, the words and pictures don't match, like the words say it's sunny, but the picture shows rain. You need to use your smarts to spot the sarcasm. That's what the IRONIC framework does, helping computers be as clever as you in spotting these mismatches.
Glossary
Multi-modal
Involves multiple forms of data, such as images and text.
Analyzing image-text relations in sarcasm detection.
Coherence Relations
A theory describing the relationship between different communicative components.
Used to analyze image-text incongruencies.
Zero-shot Learning
Generalizing to new tasks without explicit training data.
IRONIC's application in multi-modal sarcasm detection.
Sarcasm Detection
The process of identifying sarcastic intent in language.
The main task of the IRONIC framework.
Interpretative Reasoning
Combines multi-modal inputs and generated rationales for reasoning.
A key step in the IRONIC framework.
Open Questions Unanswered questions from this research
- 1 How to effectively apply the IRONIC framework in multi-lingual or code-mixed environments?
- 2 How to improve CR prediction accuracy to enhance sarcasm detection?
Applications
Immediate Applications
Social Media Analysis
Helps analyze sarcastic content on social media, improving sentiment analysis accuracy.
Long-term Vision
Human-Computer Interaction
Enhances computer understanding of human language, improving the naturalness of human-computer interaction.
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