Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities

TL;DR

This paper introduces a novel translation-based approach to learn robust joint representations for multimodal sentiment analysis, allowing effective predictions even with missing or noisy modalities, and achieves state-of-the-art results.

Contribution

It proposes a translation and cycle consistency framework that learns joint representations from a single modality, enhancing robustness and performance in multimodal sentiment analysis.

Findings

Achieves state-of-the-art results on multiple datasets

Models remain robust with missing or noisy modalities

Learns discriminative joint representations with fewer modalities

Abstract

Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work learns joint representations by requiring all modalities as input and as a result, the learned representations may be sensitive to noisy or missing modalities at test time. With the recent success of sequence to sequence (Seq2Seq) models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time. In this paper, we propose a method to learn robust joint representations by translating between modalities. Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input. We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities. Once our translation model is trained with paired multimodal data, we only need data from the source modality at test time for final sentiment prediction. This ensures that our model remains robust from perturbations or missing information in the other modalities. We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube. Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to missing or perturbed modalities.