Think Twice Before You Judge: Mixture of Dual Reasoning Experts for Multimodal Sarcasm Detection

TL;DR

This paper introduces MiDRE, a novel multimodal sarcasm detection model that combines internal and external reasoning experts with adaptive gating, leveraging structured rationales to improve understanding of sarcasm in image-text social media posts.

Contribution

MiDRE is the first model to integrate internal and external reasoning experts with adaptive gating for multimodal sarcasm detection, utilizing structured rationales from large vision-language models.

Findings

MiDRE outperforms baseline models on benchmark datasets.

External rationales significantly improve sarcasm detection accuracy.

The adaptive gating mechanism effectively balances internal and external reasoning.

Abstract

Multimodal sarcasm detection has attracted growing interest due to the rise of multimedia posts on social media. Understanding sarcastic image-text posts often requires external contextual knowledge, such as cultural references or commonsense reasoning. However, existing models struggle to capture the deeper rationale behind sarcasm, relying mainly on shallow cues like image captions or object-attribute pairs from images. To address this, we propose \textbf{MiDRE} (\textbf{Mi}xture of \textbf{D}ual \textbf{R}easoning \textbf{E}xperts), which integrates an internal reasoning expert for detecting incongruities within the image-text pair and an external reasoning expert that utilizes structured rationales generated via Chain-of-Thought prompting to a Large Vision-Language Model. An adaptive gating mechanism dynamically weighs the two experts, selecting the most relevant reasoning path. Unlike prior methods that treat external knowledge as static input, MiDRE selectively adapts to when such knowledge is beneficial, mitigating the risks of hallucinated or irrelevant signals from large models. Experiments on two benchmark datasets show that MiDRE achieves superior performance over baselines. Various qualitative analyses highlight the crucial role of external rationales, revealing that even when they are occasionally noisy, they provide valuable cues that guide the model toward a better understanding of sarcasm.