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

• URL: http://arxiv.org/abs/2310.05804v2
• Date: Thu, 14 Dec 2023 13:07:45 GMT
• Title: Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis
• Authors: Haoyu Zhang, Yu Wang, Guanghao Yin, Kejun Liu, Yuanyuan Liu, Tianshu Yu
• Abstract summary: We present Adaptive Language-guided Multimodal Transformer (ALMT) ALMT incorporates an Adaptive Hyper-modality Learning (AHL) module to learn an irrelevance/conflict-suppressing representation. ALMT achieves state-of-the-art performance on several popular datasets.
• Score: 22.012103941836838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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