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：序列生成模型日益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。语义解析旨在现实世界中执行动作这一特性，促使开发安全系统，进而使得测量校准（安全的核心组成部分）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校准情况，发现其因模型和数据集而异。随后，我们分析了与校准误差相关的因素，并发布了两个解析数据集基于置信度的新挑战子集。为促进校准评估纳入语义解析流程，我们发布了一个用于计算校准度量的库。