  Multimodal fusion is considered a key step in multimodal tasks such as
sentiment analysis, emotion detection, question answering, and others. Most of
the recent work on multimodal fusion does not guarantee the fidelity of the
multimodal representation with respect to the unimodal representations. In this
paper, we propose a variational autoencoder-based approach for modality fusion
that minimizes information loss between unimodal and multimodal
representations. We empirically show that this method outperforms the
state-of-the-art methods by a significant margin on several popular datasets.
