  Multimodal sentiment analysis has been studied under the assumption that all
modalities are available. However, such a strong assumption does not always
hold in practice, and most of multimodal fusion models may fail when partial
modalities are missing. Several works have addressed the missing modality
problem; but most of them only considered the single modality missing case, and
ignored the practically more general cases of multiple modalities missing. To
this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE)
network to handle the problem of missing uncertain modalities. Specifically, we
design a tag encoding module to cover both the single modality and multiple
modalities missing cases, so as to guide the network's attention to those
missing modalities. Besides, we adopt a new space projection pattern to align
common vectors. Then, a Transformer encoder-decoder network is utilized to
learn the missing modality features. At last, the outputs of the Transformer
encoder are used for the final sentiment classification. Extensive experiments
are conducted on CMU-MOSI and IEMOCAP datasets, showing that our method can
achieve significant improvements compared with several baselines.
