Automating spoken dialogue management design using machine learning: An industry perspective

Abstract

In designing a spoken dialogue system, developers need to specify the actions a system should take in response to user speech input and the state of the environment based on observed or inferred events, states, and beliefs. This is the fundamental task of dialogue management. Researchers have recently pursued methods for automating the design of spoken dialogue management using machine learning techniques such as reinforcement learning. In this paper, we discuss how dialogue management is handled in industry and critically evaluate to what extent current state-of-the-art machine learning methods can be of practical benefit to application developers who are deploying commercial production systems. In examining the strengths and weaknesses of these methods, we highlight what academic researchers need to know about commercial deployment if they are to influence the way industry designs and practices dialogue management.