A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems
Craig Thomson, Ehud Reiter · 2020
Most Natural Language Generation systems need to produce accurate texts.We propose a methodology for high-quality human evaluation of the accuracy of generated texts, which is intended to serve as a gold-standard for accuracy evaluations of data-to-text systems.We use our methodology to evaluate the accuracy of computer generated basketball summaries.We then show how our gold standard evaluation can be used to validate automated metrics.