These are very strong positive correlations and they show that MERT tends to learn SMT parameters that yield translations preserving the verbosity, e.g., lower verbosity on the tuning dataset will yield test-time translations that are less verbose, while higher verbosity on the tuning dataset will yield test-time translations that are more verbose.
mERT learns to generate a fixed number of words per input word.