Unsupervised learning and modeling of knowledge and intent for spoken dialogue systems

Abstract

Spoken dialogue systems (SDS) are rapidly appearing in various smart devices (smartphone, smart-TV, in-car navigating system, etc). The key role in a successful SDS is a spoken language understanding (SLU) component, which parses user utterances into semantic concepts in order to understand users' intentions. However, such semantic concepts and their structure are manually created by experts, and the annotation process results in extremely high cost and poor scalability in system development. Therefore, the dissertation focuses on improving SDS generalization and scalability by automatically inferring domain knowledge and learning structures from unlabeled conversations through a matrix factorization (MF) technique. With the automatically acquired semantic concepts and structures, we further investigate whether such information can be utilized to effectively understand user utterances and then show the feasibility of reducing human effort during SDS development.