  Multimodal Sentiment Analysis (MSA) aims to mine sentiment information from
text, visual, and acoustic modalities. Previous works have focused on
representation learning and feature fusion strategies. However, most of these
efforts ignored the disparity in the semantic richness of different modalities
and treated each modality in the same manner. That may lead to strong
modalities being neglected and weak modalities being overvalued. Motivated by
these observations, we propose a Text-oriented Modality Reinforcement Network
(TMRN), which focuses on the dominance of the text modality in MSA. More
specifically, we design a Text-Centered Cross-modal Attention (TCCA) module to
make full interaction for text/acoustic and text/visual pairs, and a Text-Gated
Self-Attention (TGSA) module to guide the self-reinforcement of the other two
modalities. Furthermore, we present an adaptive fusion mechanism to decide the
proportion of different modalities involved in the fusion process. Finally, we
combine the feature matrices into vectors to get the final representation for
the downstream tasks. Experimental results show that our TMRN outperforms the
state-of-the-art methods on two MSA benchmarks.
