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Improvements in text generation technologies such as machine translation havenecessitated more costly and time-consuming human evaluation procedures toensure an accurate signal. We investigate a simple way to reduce cost byreducing the number of text segments that must be annotated in order toaccurately predict a score for a complete test set. Using a sampling approach,we demonstrate that information from document membership and automatic metricscan help improve estimates compared to a pure random sampling baseline. Weachieve gains of up to 20% in average absolute error by leveraging stratifiedsampling and control variates. Our techniques can improve estimates made from afixed annotation budget, are easy to implement, and can be applied to anyproblem with structure similar to the one we study.

Toward More Effective Human Evaluation for Machine Translation