  Current practices in metric evaluation focus on one single dataset, e.g.,
Newstest dataset in each year's WMT Metrics Shared Task. However, in this
paper, we qualitatively and quantitatively show that the performances of
metrics are sensitive to data. The ranking of metrics varies when the
evaluation is conducted on different datasets. Then this paper further
investigates two potential hypotheses, i.e., insignificant data points and the
deviation of Independent and Identically Distributed (i.i.d) assumption, which
may take responsibility for the issue of data variance. In conclusion, our
findings suggest that when evaluating automatic translation metrics,
researchers should take data variance into account and be cautious to claim the
result on a single dataset, because it may leads to inconsistent results with
most of other datasets.
