In this work we examine user adaptation to a dialog system’s choice of realization of task-related concepts. We analyze forms of the time concept in the Let’s Go! spoken dialog system. We find that users adapt to the system’s choice of time form. We also find that user adaptation is affected by perceived system adaptation. This means that dialog systems can guide users ’ word choice and can adapt their own recognition models to gain improved ASR accuracy. 1

c-rater is Educational Testing Service’s technology for the content scoring of short student responses. A major step in the scoring process is Model Building where variants of model answers are generated that correspond to the rubric for each item or test question. Until recently, Model Building was knowledge-engineered (KE) and hence labor and time intensive. In this paper, we describe our approach to automating Model Building in c-rater. We show that c-rater achieves comparable accuracy on automatically built and KE models.

This paper presents a progressively challenging series of experiments that investigate clarification subdialogues to resolve the words in noisy transcriptions of user utterances. We focus on user utterances where the user’s specific intent requires little additional inference, given sufficient understanding of the form. We learned decision-making strategies for a dialogue manager from run-time features of our spoken dialogue system and from observation of human wizards we had embedded within it. Results show that noisy ASR can be resolved based on predictions from context about what a user might say, and that dialogue management strategies for clarifications of linguistic form benefit from access to features from spoken language understanding. 1

Proceedings of the