Conversational Turn

Conversational turn analysis focuses on understanding the structure and dynamics of conversations, aiming to improve both human-computer interaction and our scientific understanding of social interaction. Current research emphasizes developing algorithms to automatically segment conversations into turns, distinguishing between primary and secondary utterances, and leveraging large language models (LLMs) to improve the quality and coherence of multi-turn interactions, often incorporating techniques like reinforcement learning and semi-supervised learning. These advancements are significant for improving the performance of dialogue systems, enabling more nuanced analysis of human communication, and facilitating interdisciplinary research across social sciences, computer science, and linguistics.