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Mutual-Enhanced Incongruity Learning Network

The Mutual-Enhanced Incongruity Learning Network is a multimodal deep learning architecture designed for sarcasm detection that identifies sarcastic content by capturing discrepancies within and between different data modalities, such as text and images. The network operates by integrating a global incongruity learning module with a local semantic-guided incongruity learning module, allowing it to focus on fine-grained critical features while maintaining broader contextual awareness. By utilizing a mutual enhancement mechanism, the framework exploits the consistency between local and global representations to filter out irrelevant information, mitigate noise, and accurately model subtle semantic contradictions that characterize sarcasm.

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Mutual-Enhanced Incongruity Learning Network for Multi-Modal Sarcasm Detection

Mutual-Enhanced Incongruity Learning Network for Multi-Modal Sarcasm Detection

Yang Qiao, Liqiang Jing, Xuemeng Song, Xiaolin Chen, Lei Zhu, Liqiang Nie

OrganizationsHarbin Institute of TechnologySchool of Computer Science and TechnologySchool of Information Science and EngineeringSchool of SoftwareShandong Normal UniversityShandong University

Why you should read this

Presents a multi-modal sarcasm detection network that combines local graph-based semantic reasoning with global cross-attention fusion to capture incongruities across text and images while using mutual learning to transfer knowledge between the two perspectives.

Sarcasm is a sophisticated linguistic phenomenon that is prevalent on today’s social media platforms. Multi-modal sarcasm detection aims to identify whether a given sample with multi-modal information (i.e., text and image) is sarcastic. This task’s key lies in capturing both inter- and intra-modal incongruities within the same context. Although existing methods have achieved compelling success, they are disturbed by irrelevant information extracted from the whole image or text, or overlooking some important information due to the incomplete input. To address these limitations, we propose a Mutual-enhanced Incongruity Learning Network for multi-modal sarcasm detection, named MILNet. In particular, we design a local semantic-guided incongruity learning module and a global incongruity learning module. Moreover, we introduce a mutual enhancement module to take advantage of the underlying consistency between the two modules to boost the performance. Extensive experiments on a widely-used dataset demonstrate the superiority of our model over cutting-edge methods.

Added

2026-09-26