Robust Conversational AI with Grounded Text Generation

The long-term mission of conversational AI research is to develop at scale conversational assistant systems, also known as task-oriented bots or task bots in short, which are robust enough that (1) they can help users accomplish various tasks ranging from question answering and restaurant reservation to travel planning, (2) their responses are always interpretable, controllable, and reliable, even in a highly dynamic environment (e.g., due to users changing back and forth among different tasks and topics), and (3) they can transfer the knowledge and skills learned in one task to other tasks. Despite decades of research, the mission remains unfulfilled. Almost all task bots used in real-world applications are developed using task-specific, handcrafted rules and programs - an approach that fundamentally does not scale. Although machine learning methods are critical to the development of many robust NLP systems, such as machine translation and speech recognition, they play a far less important role in building task bots. For example, deep-learning based neural approaches to conversational AI, which become increasingly important as a research area [20], have not widely used for building commercial task bots yet because they are not robust enough.