Causal Emotion Entailment

Causal Emotion Entailment (CEE) focuses on identifying the conversational utterances that trigger specific emotions in a target utterance, aiming to understand the causal relationships between emotions and their antecedents. Current research emphasizes leveraging graph-based models and neural networks, including transformer-based encoders and graph convolutional networks, to capture complex relationships within conversational contexts and incorporate multimodal information (text and potentially other modalities). This research is significant for advancing natural language processing and improving human-computer interaction by enabling more nuanced understanding of emotional dynamics in conversations, leading to more empathetic and effective AI systems.