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Found 6 papers, 2 papers with code
E-ICL: Enhancing Fine-Grained Emotion Recognition through the Lens of Prototype Theory
Specifically, we conduct extensive pilot experiments and find that ICL conforms to the prototype theory on fine-grained emotion recognition.
Uncertainty Quantification on Graph Learning: A Survey
Graphical models, including Graph Neural Networks (GNNs) and Probabilistic Graphical Models (PGMs), have demonstrated their exceptional capabilities across numerous fields.
An Iterative Associative Memory Model for Empathetic Response Generation
Empathetic response generation aims to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses.
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses.
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network
Our method dynamically updates the representation of the knowledge according to the structural information of the constructed sub-graph.
