Neural Dialog Model

Neural dialog models aim to create computer systems capable of engaging in natural, coherent conversations. Current research focuses on improving response diversity and quality through techniques like variational autoencoders with diffusion priors, selective data augmentation to optimize training data, and incorporating external knowledge sources via knowledge injection or internalization. These advancements address limitations such as generic responses and insufficient knowledge representation, leading to more engaging and informative conversational agents with applications in various fields, including customer service and personalized education.