  Multimodal sentiment analysis (MSA) is an important way of observing mental
activities with the help of data captured from multiple modalities. However,
due to the recording or transmission error, some modalities may include
incomplete data. Most existing works that address missing modalities usually
assume a particular modality is completely missing and seldom consider a
mixture of missing across multiple modalities. In this paper, we propose a
simple yet effective meta-sampling approach for multimodal sentiment analysis
with missing modalities, namely Missing Modality-based Meta Sampling (M3S). To
be specific, M3S formulates a missing modality sampling strategy into the modal
agnostic meta-learning (MAML) framework. M3S can be treated as an efficient
add-on training component on existing models and significantly improve their
performances on multimodal data with a mixture of missing modalities. We
conduct experiments on IEMOCAP, SIMS and CMU-MOSI datasets, and superior
performance is achieved compared with recent state-of-the-art methods.
