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Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis
Haoyu Zhang, Yu Wang, Guanghao Yin, Kejun Liu, Yuanyuan Liu, Tianshu Yu
TL;DR
MSA can be hindered by sentiment-irrelevant and conflicting information across modalities. ALMT uses language-guided Adaptive Hyper-modality Learning to suppress such information before cross-modal fusion, and achieves state-of-the-art performance on several datasets. The authors report that ablations validate the mechanism, while performance may remain limited by large model size and small sentiment datasets.
Problem
Sentiment-irrelevant, redundant, and conflicting information across modalities can hinder further MSA improvements, especially in visual and audio features.
Method
ALMT uses language features at different scales to guide visual and audio features into a hyper-modality representation, then applies cross-modality fusion with language as query.
Results
ALMT achieves state-of-the-art performance on several public MSA datasets, including MOSI, MOSEI, and CH-SIMS.
Takeaways & Limitations
AHL explicitly suppresses redundancy and conflict in auxiliary modalities while supporting complementary multimodal sentiment representations.
Takeaways & Limitations
ALMT has many parameters and may be limited by small sentiment datasets, with relatively small improvements on MAE and Corr compared with classification metrics.
Abstract
from arXiv · showhide
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.
1 Introduction
MSA benefits from multimodal information, but sentiment-irrelevant, redundant, and conflicting content in auxiliary modalities can limit performance. ALMT addresses this issue by using language-guided hyper-modality learning and reports strong benchmark performance.
- Language is typically the dominant modality in MSA, while audio and visual modalities contribute less to performance.
- Sentiment-irrelevant and conflicting information in visual and audio modalities can limit MSA performance, and prior work had not explicitly addressed it.
- ALMT first transforms modalities into a unified form to suppress redundancy and compress long sequences for efficient computation.
- AHL uses language features at different scales to guide visual and audio features into a hyper-modality representation that complements language.
- ALMT achieves state-of-the-art performance on several public datasets and is supported by empirical analyses of the approach.
2 Related Work
Prior MSA research emphasizes either modality representation learning or multimodal fusion. Transformer-based methods model cross-modal relationships, while the proposed pipeline combines low-redundancy embedding, language-guided hyper-modality learning, and cross-modality fusion.
- Multimodal Sentiment Analysis: Previous MSA methods mainly center on refined modality representations or sophisticated multimodal fusion mechanisms.
- Representation Learning: Representation-learning methods use metric learning, adversarial learning, or hierarchical mutual-information maximization to improve multimodal fusion.
- Multimodal Fusion: Fusion-centered methods include tensor fusion and multimodal Transformers for modeling relationships, sequence alignment, and long-range cross-modal dependencies.
- Transformers: Transformer blocks aggregate information across sequence tokens and are widely used in MSA for feature extraction, representation learning, and fusion.
- Proposed Pipeline: ALMT embeds modalities with low redundancy, learns a language-guided hyper-modality representation, and fuses it with language features for MSA.
3 Method
ALMT unifies language, audio, and visual inputs, then uses language features at different scales to guide adaptive hyper-modality learning. Cross-modality fusion combines the resulting hyper-modality with language features for sentiment prediction, using a simple sentiment loss.
- 3.3 Modality Embedding: ALMT first transforms language, audio, and visual sequences into unified features using modality-specific Transformer layers and initialized low-dimensional tokens.The unified features have size T × d; in practice, T = 8 and d = 128.
- 3.3 Modality Embedding: Initialized tokens receive essential modality information, reducing sentiment-irrelevant redundancy while compressing long sequences for more efficient computation.The modality embedding uses a one-layer Vision Transformer-style structure.
- 3.4 Adaptive Hyper-modality Learning: AHL learns language features at different scales and uses them to adaptively derive a refined hyper-modality representation from visual and audio features.The AHL module consists of two Transformer layers for language-feature learning and three AHL layers for hyper-modality learning.
- 3.4 Adaptive Hyper-modality Learning: Language features serve as queries to compute similarity weights for audio and visual features, which are then combined into hyper-modality features.The audio and visual similarity matrices are denoted by α and β, respectively.
- 3.5 Multimodal Fusion and Output: CrossTrans uses language features as queries and hyper-modality features as keys and values to produce a joint multimodal representation for final sentiment analysis.An initialized token is concatenated with the high-scale language feature before fusion, and a classifier operates on the resulting representation.
- 3.6 Overall Learning Objectives: ALMT optimizes a single sentiment analysis loss, making training easier than methods with multiple optimization objectives because no extra hyper-parameters require tuning.The loss is computed from sample sentiment labels and ALMT predictions.
4 Experiments
Experiments on MOSI, MOSEI, and CH-SIMS evaluate ALMT against established metrics and state-of-the-art methods, with ablations examining modalities, components, attention settings, robustness, and efficiency. ALMT achieves strong benchmark performance, while analyses support language-guided hyper-modality learning for suppressing irrelevant or conflicting auxiliary information.
- Experimental Setup: Experiments cover MOSI, MOSEI, and CH-SIMS using classification accuracy, F1, MAE, and correlation metrics.The evaluation includes Acc-2, F1, Acc-3, Acc-5, Acc-7, MAE, and Corr, with dataset-specific Acc-2 and F1 calculations on MOSI and MOSEI.
- Performance Comparison: ALMT achieves state-of-the-art performance in almost all MOSI and MOSEI metrics, including a 1.69% relative Acc-7 improvement over CHFN on MOSI.The authors associate this result with eliminating redundancy in auxiliary modalities.
- Performance Comparison: ALMT achieves state-of-the-art performance across CH-SIMS metrics, improving Acc-2 by 1.44% and F1 by 1.40% over Self-MM.The authors report this result on the more complex CH-SIMS scenarios.
- Ablation Studies: Removing AHL significantly decreases performance across metrics, supporting its role in reducing sentiment-irrelevant redundancy in visual and audio modalities.Performance also drops when the fusion Transformer or Modality Embedding is removed, supporting their contribution to exploring modality sentiment information.
- Ablation Studies: ALMT performs best when hyper-modality features are aligned to language features and all language-feature scales guide hyper-modality learning.The authors attribute the alignment result to language information being relatively clean and sentiment-relevant.
- Efficiency and Analysis: ALMT reports Acc-7 of 49.42% with 2.50M computational cost, indicating a trade-off between accuracy and computational burden.Additional analyses report stronger visual attention, noise suppression, converged hyper-modality distributions, stable validation behavior, and modality-dependent fusion settings.
5 Conclusion
ALMT is proposed to model sentiment cues more effectively for robust Multimodal Sentiment Analysis. Its suppression of redundant visual and audio information is associated with improved performance across popular datasets.
- ALMT is proposed to better model sentiment cues for robust Multimodal Sentiment Analysis.
- Suppressing adverse effects from redundant visual and audio information supports more robust sentiment analysis.
- ALMT achieves highly improved performance on several popular datasets.
- The paper provides in-depth studies examining reasons behind ALMT’s effectiveness.
Limitations
ALMT is a parameter-heavy Transformer model whose performance may be constrained by the small size of current sentiment datasets. Fine-grained regression metrics may therefore improve less than classification metrics.
- ALMT usually has a large number of parameters and requires comprehensive training.
- Small sentiment datasets may limit ALMT’s performance.
- MAE and Corr may require more training data than Acc-7 and Acc-2, resulting in relatively small improvements.
A Hyper-parameters
Key hyper-parameters are selected using the CH-SIMS validation set.
- Key hyper-parameters are selected on the CH-SIMS validation set.
A.1 Overview
ALMT experiments use a specified computing setup, and key parameters are reported across different datasets. Most hyper-parameters remain consistent across datasets.
- The method is implemented in PyTorch and evaluated on a system with an Intel Xeon 6240C CPU, 128GB memory, and an NVIDIA GeForce RTX 3090.
- Key ALMT parameters used on different datasets are shown in Table 9.
- Most hyper-parameters are the same across datasets, indicating that ALMT does not require complex hyper-parameter adjustment.
A.2 Effects of Length Settings of Modality Feature
The study evaluates modality-embedding token length and finds that T=8 provides the best performance–computation balance for ALMT.
- Figure 8 evaluates how modality-embedding token sequence length affects Accuracy and MAE on CH-SIMS.
- Performance changes significantly as the token length hyperparameter varies, with similar behavior observed on MOSI and MOSEI.
- T=8 is selected because it offers strong performance at lower computational cost, although MAE is not best when Acc-5 peaks at T=32.
A.3 Effects of Depth Settings of AHL
The study examines AHL depth on CH-SIMS and selects depth 3, which achieves the best Acc-5 and MAE performance and shows similar behavior on MOSI and MOSEI.
- Depth 3 achieves the best performance on the two most difficult metrics, Acc-5 and MAE.
- The same AHL depth of 3 is used on MOSI and MOSEI because similar performance patterns are observed there.
- Figure 9 evaluates different AHL depths using Accuracy and MAE curves on CH-SIMS.
A.4 Effects of Depth Settings of Fusion Transformer
The study evaluates cross-modality fusion Transformer depth and chooses settings that balance performance with computational cost across CH-SIMS, MOSI, and MOSEI.
- On CH-SIMS, depth 3 gives the best Acc-5, whereas depth 5 gives the best MAE.
- Depth 4 is selected on CH-SIMS to balance performance and model computation.
- The fusion Transformer depth is set to 2 on MOSI and 4 on MOSEI following the same performance–computation balancing rule.
- Figure 10 evaluates fusion Transformer depth using Accuracy and MAE curves on CH-SIMS.