  Automatic evaluation metrics capable of replacing human judgments are
critical to allowing fast development of new methods. Thus, numerous research
efforts have focused on crafting such metrics. In this work, we take a step
back and analyze recent progress by comparing the body of existing automatic
metrics and human metrics altogether. As metrics are used based on how they
rank systems, we compare metrics in the space of system rankings. Our extensive
statistical analysis reveals surprising findings: automatic metrics -- old and
new -- are much more similar to each other than to humans. Automatic metrics
are not complementary and rank systems similarly. Strikingly, human metrics
predict each other much better than the combination of all automatic metrics
used to predict a human metric. It is surprising because human metrics are
often designed to be independent, to capture different aspects of quality, e.g.
content fidelity or readability. We provide a discussion of these findings and
recommendations for future work in the field of evaluation.
