Conversation Graph

Conversation graphs represent conversational interactions as networks, aiming to improve understanding of complex dialogue dynamics and emotion recognition. Current research focuses on developing sophisticated graph neural network architectures, often incorporating attention mechanisms and metric learning, to model contextual information and speaker interactions within these graphs, addressing challenges like limited addressee information and long-term dependencies. These advancements have led to improved performance in tasks such as emotion recognition and multi-party conversation generation, with implications for applications in areas like healthcare, education, and chatbot development.