Multimodal learning has exhibited a significant advantage in affective analysis tasks owing to the comprehensive information of various modalities, particularly the complementary information. Thus, many emerging studies focus on disentangling the modality-invariant and modality-specific representations from input data and then fusing them for prediction. However, our study shows that modality-specific representations may contain information that is irrelevant or conflicting with the tasks, which downgrades the effectiveness of learned multimodal representations. We revisit the disentanglement issue, and propose a novel triple disentanglement approach, TriDiRA, which disentangles the modality-invariant, effective modality-specific and ineffective modality-specific representations from input data. By fusing only the modality-invariant and effective modality-specific representations, TriDiRA can significantly alleviate the impact of irrelevant and conflicting information across modalities during model training. Extensive experiments conducted on four benchmark datasets demonstrate the effectiveness and generalization of our triple disentanglement, which outperforms SOTA methods.

翻译：多模态学习凭借各类模态提供的全面信息，特别是互补信息，在情感分析任务中展现出显著优势。因此，许多新兴研究致力于从输入数据中解耦出模态不变表示与模态特定表示，再对其进行融合预测。然而，本研究表明模态特定表示可能包含与任务无关或冲突的信息，这会降低所学多模态表示的有效性。我们重新审视了解耦问题，并提出了一种新颖的三重解耦方法TriDiRA，该方法能从输入数据中解耦出模态不变表示、有效模态特定表示和无效模态特定表示。通过仅融合模态不变表示与有效模态特定表示，TriDiRA能显著减轻模型训练过程中跨模态的无关信息和冲突信息的影响。在四个基准数据集上开展的广泛实验证明了我们三重解耦方法的效果与泛化能力，其性能优于当前最优方法。