  Multimodal sentiment analysis is a trending area of research, and the
multimodal fusion is one of its most active topic. Acknowledging humans
communicate through a variety of channels (i.e visual, acoustic, linguistic),
multimodal systems aim at integrating different unimodal representations into a
synthetic one. So far, a consequent effort has been made on developing complex
architectures allowing the fusion of these modalities. However, such systems
are mainly trained by minimising simple losses such as $L_1$ or cross-entropy.
In this work, we investigate unexplored penalties and propose a set of new
objectives that measure the dependency between modalities. We demonstrate that
our new penalties lead to a consistent improvement (up to $4.3$ on accuracy)
across a large variety of state-of-the-art models on two well-known sentiment
analysis datasets: \texttt{CMU-MOSI} and \texttt{CMU-MOSEI}. Our method not
only achieves a new SOTA on both datasets but also produces representations
that are more robust to modality drops. Finally, a by-product of our methods
includes a statistical network which can be used to interpret the high
dimensional representations learnt by the model.
