Natural language generation (NLG) has received increasing attention, which has highlighted evaluation as a central methodological concern. Since human evaluations for these systems are costly, automatic metrics have broad appeal in NLG. Research in language generation often finds situations where it is appropriate to apply existing metrics or propose new ones. The application of these metrics are entirely dependent on validation studies - studies that determine a metric's correlation to human judgment. However, there are many details and considerations in conducting strong validation studies. This document is intended for those validating existing metrics or proposing new ones in the broad context of NLG: we 1) begin with a write-up of best practices in validation studies, 2) outline how to adopt these practices, 3) conduct analyses in the WMT'17 metrics shared task\footnote{Our jupyter notebook containing the analyses is available at \url{https://github.com}}, and 4) highlight promising approaches to NLG metrics 5) conclude with our opinions on the future of this area.

本文主要探讨自然语言生成领域中测评方法中的自动指标的应用和验证，提出了验证研究的最佳实践，并在WMT'17度量共享任务中进行了分析，同时也突出了未来的发展方向。

关于自然语言生成评估中自动度量标准进行更好验证研究