RAMer achieves state-of-the-art multi-label emotion recognition on three benchmarks by combining reconstruction-based adversarial training, contrastive learning, a personality cue, and a stack shuffle augmentation to handle missing modalities in multi-party conversations.
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RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion Recognition
RAMer achieves state-of-the-art multi-label emotion recognition on three benchmarks by combining reconstruction-based adversarial training, contrastive learning, a personality cue, and a stack shuffle augmentation to handle missing modalities in multi-party conversations.