Calibrated Interpretation: Confidence Estimation in Semantic Parsing

Task-oriented semantic parsing is increasingly being used in user-facing applications, making measuring the calibration of parsing models especially important. We examine the calibration characteristics of six models across three model families on two common English semantic parsing datasets, finding that many models are reasonably well-calibrated and that there is a trade-off between calibration and performance. Based on confidence scores across three models, we propose and release new challenge splits of the two datasets we examine. We then illustrate the ways a calibrated model can be useful in balancing common trade-offs in task-oriented parsing. In a simulated annotator-in-the-loop experiment, we show that using model confidence allows us to improve performance by 9.6% (absolute) with interactions on only 2.2% of tokens. Using sequence-level confidence scores, we then examine how we can optimize trade-off between a parser's usability and safety. We show that confidence-based thresholding can reduce the number of incorrect low-confidence programs executed by 76%; however, this comes at a cost to usability. We propose the DidYouMean system which balances usability and safety. We conclude by calling for calibration to be included in the evaluation of semantic parsing systems, and release a library for computing calibration metrics.