Multimodal Sentiment Analysis with Missing Modality: A
  Knowledge-Transfer Approach
Weide Liu Huijing Zhan Hao Chen Fengmao Lv
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.