  Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing rich
information from multiple sources (e.g., language, video, and audio), the
potential sentiment-irrelevant and conflicting information across modalities
may hinder the performance from being further improved. To alleviate this, we
present Adaptive Language-guided Multimodal Transformer (ALMT), which
incorporates an Adaptive Hyper-modality Learning (AHL) module to learn an
irrelevance/conflict-suppressing representation from visual and audio features
under the guidance of language features at different scales. With the obtained
hyper-modality representation, the model can obtain a complementary and joint
representation through multimodal fusion for effective MSA. In practice, ALMT
achieves state-of-the-art performance on several popular datasets (e.g., MOSI,
MOSEI and CH-SIMS) and an abundance of ablation demonstrates the validity and
necessity of our irrelevance/conflict suppression mechanism.
