Controllable Dialogue Generation

Controllable dialogue generation aims to create chatbots and conversational agents that produce responses adhering to specific constraints, such as expressing particular emotions, personalities, or dialogue acts. Current research focuses on improving the controllability of these systems, particularly handling multiple attributes simultaneously and generalizing to unseen combinations, often employing techniques like prompt engineering, disentangled representations, and weighted decoding within neural language models or hybrid rule-based/neural architectures. This field is significant because it addresses limitations in current dialogue systems, paving the way for more sophisticated and reliable conversational AI with applications in various domains, including customer service, education, and mental health support.