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.