Dialog Model

Dialog models aim to create systems capable of engaging in natural, coherent conversations with humans, encompassing both task-oriented and open-domain interactions. Current research emphasizes improving model robustness to noisy or incomplete inputs (e.g., speech recognition errors, missing visual data), leveraging multimodal information (text, images, audio), and enhancing contextual understanding through techniques like graph neural networks and contrastive learning. These advancements are crucial for building more effective and human-like conversational AI agents with applications ranging from customer service chatbots to collaborative problem-solving tools.