Topic Shift

Topic shift detection in dialogues aims to identify changes in conversation subject, a crucial task for building more natural and engaging conversational AI. Current research focuses on developing models that effectively capture topic granularity at multiple levels (e.g., utterance, turn, overall topic) using techniques like prompt engineering and contrastive learning, often leveraging knowledge graphs to improve data generation and model training. These advancements are vital for improving the performance of dialogue systems and creating more human-like conversational agents, impacting fields such as chatbot development and human-computer interaction.