Sequence generation models are increasingly being used to translate natural language into programs, i.e. to perform executable semantic parsing. The fact that semantic parsing aims to predict programs that can lead to executed actions in the real world motivates developing safe systems. This in turn makes measuring calibration -- a central component to safety -- particularly important. We investigate the calibration of popular 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：序列生成模型越来越多地被用于将自然语言翻译成程序，即执行可执行的语义解析。语义解析旨在预测可导致现实世界中可执行动作的程序，这一事实促使了安全系统的发展。这反过来使得测量校准——安全性的核心组成部分——变得尤为重要。我们研究了流行生成模型在四个常见语义解析数据集上的校准情况，发现其在不同模型和数据集之间存在差异。接着，我们分析了与校准错误相关的因素，并发布了基于置信度的两个解析数据集的新挑战分割。为了促进校准在语义解析评估中的融入，我们发布了一个用于计算校准指标的库。