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We illustrate how a calibrated model can help balance common trade-offs intask-oriented parsing. In a simulated annotator-in-the-loop experiment, we showthat well-calibrated confidence scores allow us to balance cost with annotatorload, improving accuracy with a small number of interactions. We then examinehow confidence scores can help optimize the trade-off between usability andsafety. We show that confidence-based thresholding can substantially reduce thenumber of incorrect low-confidence programs executed; however, this comes at acost to usability. We propose the DidYouMean system which better balancesusability and safety.

Did You Mean...? Confidence-based Trade-offs in Semantic Parsing