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Though Multimodal Sentiment Analysis (MSA) proves effective by utilizing richinformation from multiple sources (e.g., language, video, and audio), thepotential sentiment-irrelevant and conflicting information across modalitiesmay hinder the performance from being further improved. To alleviate this, wepresent Adaptive Language-guided Multimodal Transformer (ALMT), whichincorporates an Adaptive Hyper-modality Learning (AHL) module to learn anirrelevance/conflict-suppressing representation from visual and audio featuresunder the guidance of language features at different scales. With the obtainedhyper-modality representation, the model can obtain a complementary and jointrepresentation through multimodal fusion for effective MSA. In practice, ALMTachieves state-of-the-art performance on several popular datasets (e.g., MOSI,MOSEI and CH-SIMS) and an abundance of ablation demonstrates the validity andnecessity of our irrelevance/conflict suppression mechanism.

Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis