Conversational Context

Conversational context research focuses on understanding and modeling the nuances of multi-turn interactions to improve the performance of conversational AI systems. Current efforts concentrate on developing models that effectively incorporate various contextual factors, such as speaker emotional state, dialogue history, and multimodal information (text, speech, images), often leveraging large language models (LLMs) and graph neural networks (GNNs) for improved context representation and reasoning. This research is crucial for advancing the capabilities of conversational agents in diverse applications, including question answering, speech synthesis, and emotion recognition, ultimately leading to more natural and human-like interactions.