  Our senses individually work in a coordinated fashion to express our
emotional intentions. In this work, we experiment with modeling
modality-specific sensory signals to attend to our latent multimodal emotional
intentions and vice versa expressed via low-rank multimodal fusion and
multimodal transformers. The low-rank factorization of multimodal fusion
amongst the modalities helps represent approximate multiplicative latent signal
interactions. Motivated by the work of~\cite{tsai2019MULT} and~\cite{Liu_2018},
we present our transformer-based cross-fusion architecture without any
over-parameterization of the model. The low-rank fusion helps represent the
latent signal interactions while the modality-specific attention helps focus on
relevant parts of the signal. We present two methods for the Multimodal
Sentiment and Emotion Recognition results on CMU-MOSEI, CMU-MOSI, and IEMOCAP
datasets and show that our models have lesser parameters, train faster and
perform comparably to many larger fusion-based architectures.
