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A major challenge in the field of Text Generation is evaluation because welack a sound theory that can be leveraged to extract guidelines for evaluationcampaigns. In this work, we propose a first step towards such a theory thatincorporates different sources of uncertainty, such as imperfect automatedmetrics and insufficiently sized test sets. The theory has practicalapplications, such as determining the number of samples needed to reliablydistinguish the performance of a set of Text Generation systems in a givensetting. We showcase the application of the theory on the WMT 21 andSpot-The-Bot evaluation data and outline how it can be leveraged to improve theevaluation protocol regarding the reliability, robustness, and significance ofthe evaluation outcome.

On the Effectiveness of Automated Metrics for Text Generation Systems