  Multimodal research is an emerging field of artificial intelligence, and one
of the main research problems in this field is multimodal fusion. The fusion of
multimodal data is the process of integrating multiple unimodal representations
into one compact multimodal representation. Previous research in this field has
exploited the expressiveness of tensors for multimodal representation. However,
these methods often suffer from exponential increase in dimensions and in
computational complexity introduced by transformation of input into tensor. In
this paper, we propose the Low-rank Multimodal Fusion method, which performs
multimodal fusion using low-rank tensors to improve efficiency. We evaluate our
model on three different tasks: multimodal sentiment analysis, speaker trait
analysis, and emotion recognition. Our model achieves competitive results on
all these tasks while drastically reducing computational complexity. Additional
experiments also show that our model can perform robustly for a wide range of
low-rank settings, and is indeed much more efficient in both training and
inference compared to other methods that utilize tensor representations.
