Towards Interactive Learning of Spoken Dialog Systems
Spoken dialog system is a prominent component in today’s virtual personal assistant, which enables people to perform everyday tasks by interacting with devices via voice interfaces. Recent advances in deep learning enabled new research directions for end-to-end neural network based dialog modeling. Such data-driven learning systems address many limitations of the conventional dialog systems but also introduce new challenges. In this talk, we will discuss recent research work on deep and reinforcement learning for neural dialog systems. We will further discuss how we can the challenges on learning efficiency and scalability by combining offline training and online interactive learning with human-in-the-loop.
Bing is a research scientist at Facebook working on conversational AI. His area of work focuses on machine learning for spoken language processing, natural language understanding, and dialog systems. He develops conversational AI systems that learn continuously from user interactions with weak supervision via deep and reinforcement learning. Before joining Facebook, he interned at Google Research working on end-to-end learning of neural dialog systems. Bing received his Ph.D. from Carnegie Mellon University where he worked on deep learning and reinforcement learning for task-oriented dialog systems.