  Multimodal sentiment analysis (MSA) leverages heterogeneous data sources to
interpret the complex nature of human sentiments. Despite significant progress
in multimodal architecture design, the field lacks comprehensive regularization
methods. This paper introduces PowMix, a versatile embedding space regularizer
that builds upon the strengths of unimodal mixing-based regularization
approaches and introduces novel algorithmic components that are specifically
tailored to multimodal tasks. PowMix is integrated before the fusion stage of
multimodal architectures and facilitates intra-modal mixing, such as mixing
text with text, to act as a regularizer. PowMix consists of five components: 1)
a varying number of generated mixed examples, 2) mixing factor reweighting, 3)
anisotropic mixing, 4) dynamic mixing, and 5) cross-modal label mixing.
Extensive experimentation across benchmark MSA datasets and a broad spectrum of
diverse architectural designs demonstrate the efficacy of PowMix, as evidenced
by consistent performance improvements over baselines and existing mixing
methods. An in-depth ablation study highlights the critical contribution of
each PowMix component and how they synergistically enhance performance.
Furthermore, algorithmic analysis demonstrates how PowMix behaves in different
scenarios, particularly comparing early versus late fusion architectures.
Notably, PowMix enhances overall performance without sacrificing model
robustness or magnifying text dominance. It also retains its strong performance
in situations of limited data. Our findings position PowMix as a promising
versatile regularization strategy for MSA. Code will be made available.
