Modality Agreement- and Conflict-Aware Prototype Hypergraph Learning for Multimodal Intent Understanding
Mohnish Raj, Suraj Kumar, Soumi Chattopadhayay, Chandranath Adak, Ayan Dutta
Why It Matters
What makes this one worth your time
Understanding and leveraging both agreement and conflict in multimodal data can lead to more accurate intent recognition, which is crucial for applications in human-computer interaction and AI communication systems.
MACH framework enhances multimodal intent recognition by modeling both agreement and conflict among modalities.
Summary
The paper introduces MACH, a hierarchical prototype-hypergraph framework designed to improve multimodal intent recognition by separately modeling modality agreement and conflict. It uses a dual-pathway approach to capture both consensus and discrepancies in multimodal data, with a feature-wise arbitration mechanism to balance these aspects. The framework is validated through experiments on benchmark datasets.
Key contributions
- Introduction of a hierarchical prototype-hypergraph framework for multimodal intent recognition.
- Development of a dual-pathway approach to separately model modality agreement and conflict.
- Implementation of a feature-wise arbitration mechanism to integrate agreement and conflict information.
Notable insights
- The use of prototype hypergraphs to separately model agreement and conflict in multimodal data is a novel approach.
- The feature-wise, sample-adaptive arbitration mechanism allows for dynamic balancing of agreement and conflict information.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2608.04054v1 Announce Type: cross Abstract: Multimodal intent recognition requires understanding not only what textual, acoustic, and visual signals share, but also how they disagree. Such disagreement is frequently class-informative; for example, lexical positivity accompanied by incongruent vocal or facial behavior may indicate sarcasm or taunting, yet most fusion methods either encourage modality alignment or treat inconsistency as uncertainty to be suppressed. We propose MACH (Modality Agreement- and Conflict-aware prototype Hypergraph), a hierarchical prototype-hypergraph framework that represents multimodal agreement and conflict as distinct, recurring relational structures. MACH progressively composes unimodal representations into bimodal and trimodal abstractions. At each applicable level, modality-composition anchors activate sparse agreement prototype hypergraphs that capture reusable consensus patterns, while a separate conflict pathway maps cross-modal discrepancies to dedicated conflict prototype hypergraphs. The two pathways are combined through a feature-wise, sample-adaptive arbitration mechanism, enabling the model to preserve informative disagreement while suppressing incidental modality noise. A progressive optimization strategy stabilizes the interdependent hierarchy before joint agreement-conflict learning. Experiments on benchmark datasets demonstrate the effectiveness of the proposed formulation, while component and robustness analyses validate the distinct roles of hierarchical composition, prototype-mediated semantic refinement, and agreement-conflict arbitration.