[Paper Review] Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis

Haoyu Zhang, Yu Wang+4 more|arXiv (Cornell University)|Oct 9, 2023

Multimodal Machine Learning ApplicationsComputer Science29 references3 citations

TL;DR

This paper proposes Adaptive Language-guided Multimodal Transformer (ALMT), a novel framework that uses an Adaptive Hyper-modality Learning (AHL) module to suppress sentiment-irrelevant and conflicting information in visual and audio modalities under language guidance. By learning a robust, unified hyper-modality representation, ALMT achieves state-of-the-art performance on MOSI, MOSEI, and CH-SIMS datasets with improved generalization and stability.

ABSTRACT

Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich information from multiple sources (e.g., language, video, and audio), the potential sentiment-irrelevant and conflicting information across modalities may hinder the performance from being further improved. To alleviate this, we present Adaptive Language-guided Multimodal Transformer (ALMT), which incorporates an Adaptive Hyper-modality Learning (AHL) module to learn an irrelevance/conflict-suppressing representation from visual and audio features under the guidance of language features at different scales. With the obtained hyper-modality representation, the model can obtain a complementary and joint representation through multimodal fusion for effective MSA. In practice, ALMT achieves state-of-the-art performance on several popular datasets (e.g., MOSI, MOSEI and CH-SIMS) and an abundance of ablation demonstrates the validity and necessity of our irrelevance/conflict suppression mechanism.

Motivation & Objective

• To address the adverse impact of sentiment-irrelevant and conflicting information in non-dominant modalities (e.g., visual and audio) on multimodal sentiment analysis (MSA) performance.
• To improve multimodal fusion by learning a joint, complementary representation that reduces interference from redundant or conflicting features.
• To explicitly model modality-specific disruptions (e.g., lighting, noise) through a learnable, language-guided mechanism.
• To achieve more robust and generalizable sentiment prediction by narrowing distribution gaps between modalities.
• To validate the necessity and effectiveness of the proposed irrelevance/conflict suppression mechanism through extensive ablation and visualization.

Proposed method

• Each modality (text, audio, video) is first embedded into a unified sequence representation using a Transformer with learnable class tokens, reducing redundancy and sequence length.
• An Adaptive Hyper-modality Learning (AHL) module is introduced to generate a joint hyper-modality representation from audio and visual features, guided by multi-scale language features to suppress irrelevant content.
• The AHL module uses attention mechanisms to dynamically weigh visual and audio features based on their relevance to the language modality, producing a compact, conflict-resilient representation.
• A cross-modality fusion Transformer then uses language features as queries and the hyper-modality features as keys and values to learn a joint, complementary representation for sentiment classification.
• The model is trained end-to-end with standard MSA loss functions, including regression (MAE, Corr) and classification (Acc-7, Acc-2) metrics.
• The framework is evaluated on MOSI, MOSEI, and CH-SIMS, with ablation studies and visualization to analyze robustness and modality contributions.

Experimental results

Research questions

• RQ1 How does language-guided suppression of irrelevant visual and audio features improve multimodal sentiment analysis performance?
• RQ2 To what extent does the proposed AHL module reduce inter- and intra-modality distribution gaps between audio and visual features?
• RQ3 Does the model maintain robustness when faced with noisy or irrelevant frames in visual and audio modalities?
• RQ4 How does the multi-scale language guidance in AHL affect the quality of the hyper-modality representation?
• RQ5 What is the relative contribution of visual versus audio modality to the final sentiment prediction in the proposed framework?

Key findings

• ALMT achieves state-of-the-art performance on MOSI, MOSEI, and CH-SIMS, outperforming existing methods in both classification and regression metrics.
• On CH-SIMS, ALMT achieves a MAE of 0.286 and Corr of 0.782, demonstrating strong performance on fine-grained sentiment regression.
• Ablation studies confirm that removing the AHL module leads to significant performance drops, validating its necessity for suppressing disruptive features.
• Visualization shows that attention weights between language and visual features decrease significantly when random noise is added to peak frames, confirming AHL’s ability to suppress sentiment-irrelevant information.
• t-SNE visualization confirms that hyper-modality representations from audio and visual modalities converge into a shared distribution, reducing modality discrepancy.
• Convergence analysis shows ALMT trains stably with less fluctuation and better generalization than SOTA baselines like MulT, MISA, and Self-MM on both training and validation sets.