  Multimodal machine learning is a core research area spanning the language,
visual and acoustic modalities. The central challenge in multimodal learning
involves learning representations that can process and relate information from
multiple modalities. In this paper, we propose two methods for unsupervised
learning of joint multimodal representations using sequence to sequence
(Seq2Seq) methods: a \textit{Seq2Seq Modality Translation Model} and a
\textit{Hierarchical Seq2Seq Modality Translation Model}. We also explore
multiple different variations on the multimodal inputs and outputs of these
seq2seq models. Our experiments on multimodal sentiment analysis using the
CMU-MOSI dataset indicate that our methods learn informative multimodal
representations that outperform the baselines and achieve improved performance
on multimodal sentiment analysis, specifically in the Bimodal case where our
model is able to improve F1 Score by twelve points. We also discuss future
directions for multimodal Seq2Seq methods.
