Neural Conversational

Neural conversational models aim to create AI systems capable of engaging in natural, coherent, and informative dialogue with humans. Current research focuses on improving response quality through techniques like incorporating knowledge graphs, leveraging reinforcement learning to optimize long-term conversational flow, and enhancing diversity and factuality using methods such as curriculum learning and Natural Language Inference. These advancements are significant for advancing human-computer interaction, particularly in applications like chatbots, educational tutoring systems, and human-robot interaction, where natural and engaging conversation is crucial.