  With the proliferation of user-generated online videos, Multimodal Sentiment
Analysis (MSA) has attracted increasing attention recently. Despite significant
progress, there are still two major challenges on the way towards robust MSA:
1) inefficiency when modeling cross-modal interactions in unaligned multimodal
data; and 2) vulnerability to random modality feature missing which typically
occurs in realistic settings. In this paper, we propose a generic and unified
framework to address them, named Efficient Multimodal Transformer with
Dual-Level Feature Restoration (EMT-DLFR). Concretely, EMT employs
utterance-level representations from each modality as the global multimodal
context to interact with local unimodal features and mutually promote each
other. It not only avoids the quadratic scaling cost of previous local-local
cross-modal interaction methods but also leads to better performance. To
improve model robustness in the incomplete modality setting, on the one hand,
DLFR performs low-level feature reconstruction to implicitly encourage the
model to learn semantic information from incomplete data. On the other hand, it
innovatively regards complete and incomplete data as two different views of one
sample and utilizes siamese representation learning to explicitly attract their
high-level representations. Comprehensive experiments on three popular datasets
demonstrate that our method achieves superior performance in both complete and
incomplete modality settings.
