  A major challenge in the field of Text Generation is evaluation: Human
evaluations are cost-intensive, and automated metrics often display
considerable disagreement with human judgments. In this paper, we propose a
statistical model of Text Generation evaluation that accounts for the
error-proneness of automated metrics when used to generate preference rankings
between system outputs. We show that existing automated metrics are generally
over-confident in assigning significant differences between systems in this
setting. However, our model enables an efficient combination of human and
automated ratings to remedy the error-proneness of the automated metrics. We
show that using this combination, we only require about 50% of the human
annotations typically used in evaluations to arrive at robust and statistically
significant results while yielding the same evaluation outcome as the pure
human evaluation in 95% of cases. We showcase the benefits of approach for
three text generation tasks: dialogue systems, machine translation, and text
summarization.

Computation and Language;Artificial Intelligence