Dialogue Framework

Dialogue frameworks aim to create computational models capable of engaging in natural and effective conversations with humans, encompassing both cooperative and non-cooperative interactions. Current research focuses on improving the robustness and adaptability of these frameworks, exploring diverse model architectures such as graph-based representations, transformer networks, and reinforcement learning approaches with structured policies to handle multi-domain and multi-task dialogues, and incorporating modalities beyond text like facial expressions and emotions. These advancements are crucial for developing more human-like and versatile conversational agents with applications ranging from customer service and emotional support to complex task completion.