Sequence generation models are increasingly being used to translate language into executable programs, i.e. to perform executable semantic parsing. The fact that semantic parsing aims to execute actions in the real world motivates developing safe systems, which in turn makes measuring calibration -- a central component to safety -- particularly important. We investigate the calibration of common generation models across four popular semantic parsing datasets, finding that it varies across models and datasets. We then analyze factors associated with calibration error and release new confidence-based challenge splits of two parsing datasets. To facilitate the inclusion of calibration in semantic parsing evaluations, we release a library for computing calibration metrics.

翻译：序列生成模型正越来越多地被用于将语言翻译为可执行程序，即执行可执行的语义解析。由于语义解析旨在现实世界中执行操作，这促使开发安全的系统，进而使得衡量标定——安全性的核心要素——变得尤为重要。我们研究了常见生成模型在四个流行语义解析数据集上的标定情况，发现其标定效果随模型和数据集的不同而变化。随后，我们分析了与标定误差相关的因素，并发布了两个解析数据集的新挑战性划分，这些划分基于置信度。为了促进标定在语义解析评估中的应用，我们发布了一个用于计算标定指标的库。