Implicitly-Supervised Learning in Spoken Language Interfaces: An Application to the Confidence Annotation Problem

In this paper we propose the use of a novel learning paradigm in spoken language interfaces – implicitly-supervised learning. The central idea is to extract a supervision signal online, directly from the user, from certain patterns that occur naturally in the conversation. The approach eliminates the need for developer supervision and facilitates online learning and adaptation. As a first step towards better understanding its properties, advantages and limitations, we have applied the proposed approach to the problem of confidence annotation. Experimental results indicate that we can attain performance similar to that of a fully supervised model, without any manual labeling. In effect, the system learns from its own experiences with the users.