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Multimodal machine learning is a core research area spanning the language,visual and acoustic modalities. The central challenge in multimodal learninginvolves learning representations that can process and relate information frommultiple modalities. In this paper, we propose two methods for unsupervisedlearning 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 exploremultiple different variations on the multimodal inputs and outputs of theseseq2seq models. Our experiments on multimodal sentiment analysis using theCMU-MOSI dataset indicate that our methods learn informative multimodalrepresentations that outperform the baselines and achieve improved performanceon multimodal sentiment analysis, specifically in the Bimodal case where ourmodel is able to improve F1 Score by twelve points. We also discuss futuredirections for multimodal Seq2Seq methods.

Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis