Multi Party

Multi-party conversation research focuses on understanding and modeling interactions involving multiple participants, aiming to improve computational systems' ability to process and generate responses in complex group settings. Current research emphasizes developing models that effectively handle the intricate structural and linguistic aspects of these conversations, often employing graph neural networks and transformer architectures to capture relationships between speakers and utterances, along with techniques like prompt engineering and multimodal data integration. This field is crucial for advancing human-computer interaction, particularly in applications like social robots, collaborative data analysis, and meeting summarization, where understanding and responding to group dynamics is essential.