  Automatic evaluation for open-ended natural language generation tasks remains
a challenge. Existing metrics such as BLEU show a low correlation with human
judgment. We propose a novel and powerful learning-based evaluation metric:
Perception Score. The method measures the overall quality of the generation and
scores holistically instead of only focusing on one evaluation criteria, such
as word overlapping. Moreover, it also shows the amount of uncertainty about
its evaluation result. By connecting the uncertainty, Perception Score gives a
more accurate evaluation for the generation system. Perception Score provides
state-of-the-art results on two conditional generation tasks and two
unconditional generation tasks.
