Multimodal sentiment analysis aims to identify the emotions expressed by
individuals through visual, language, and acoustic cues. However, most of the
existing research efforts assume that all modalities are available during both
training and testing, making their algorithms susceptible to the missing
modality scenario. In this paper, we propose a novel knowledge-transfer network
to translate between different modalities to reconstruct the missing audio
modalities. Moreover, we develop a cross-modality attention mechanism to retain
the maximal information of the reconstructed and observed modalities for
sentiment prediction. Extensive experiments on three publicly available
datasets demonstrate significant improvements over baselines and achieve
comparable results to the previous methods with complete multi-modality
supervision.

提出了一种新颖的知识传递网络来在不同的感知模式之间进行翻译以重构缺失的音频模式，并且采用了跨模态注意机制以保留重构和观察到的模态的最大信息，用于情感预测。通过对三个公开数据集进行大量实验证明，相较于基线方法实现了显著的改进，并在完全多模态监督方面取得了与之前方法相当的结果。