Dialogue Emotion

Dialogue emotion recognition aims to automatically identify the emotional state expressed in conversations, leveraging various modalities like text, audio, and video. Current research emphasizes multimodal approaches, often employing graph convolutional networks or transformer-based architectures to capture complex relationships between utterances, speakers, and events within a conversation, and incorporating techniques like contrastive learning and disentanglement to improve accuracy and handle ambiguity. This field is crucial for developing more emotionally intelligent AI systems with applications in areas such as mental health support, customer service, and human-computer interaction.