Intent Clustering

Intent clustering aims to automatically group user utterances into meaningful intent categories, crucial for building robust and efficient conversational AI systems. Current research focuses on improving the semantic understanding of embedding models, exploring graph-based frameworks to capture data structure and relationships for better cluster formation, and investigating the impact of different embedding techniques and clustering algorithms on performance. These advancements are vital for enhancing the accuracy and scalability of intent induction in task-oriented dialogue systems and other applications requiring automatic understanding of user intent.