  Multimodal sentiment analysis utilizes multiple heterogeneous modalities for
sentiment classification. The recent multimodal fusion schemes customize LSTMs
to discover intra-modal dynamics and design sophisticated attention mechanisms
to discover the inter-modal dynamics from multimodal sequences. Although
powerful, these schemes completely rely on attention mechanisms which is
problematic due to two major drawbacks 1) deceptive attention masks, and 2)
training dynamics. Nevertheless, strenuous efforts are required to optimize
hyperparameters of these consolidate architectures, in particular their
custom-designed LSTMs constrained by attention schemes. In this research, we
first propose a common network to discover both intra-modal and inter-modal
dynamics by utilizing basic LSTMs and tensor based convolution networks. We
then propose unique networks to encapsulate temporal-granularity among the
modalities which is essential while extracting information within asynchronous
sequences. We then integrate these two kinds of information via a fusion layer
and call our novel multimodal fusion scheme as Deep-HOSeq (Deep network with
higher order Common and Unique Sequence information). The proposed Deep-HOSeq
efficiently discovers all-important information from multimodal sequences and
the effectiveness of utilizing both types of information is empirically
demonstrated on CMU-MOSEI and CMU-MOSI benchmark datasets. The source code of
our proposed Deep-HOSeq is and available at
https://github.com/sverma88/Deep-HOSeq--ICDM-2020.
