Example: in wmt12, the gold-based LMAE for the top 20% sentences is higher than 0.8 when the system is trained only on the official train set (0% of the post-edited sentences added), but reaches 0.4 when about half of the post-edited sentences are added to the training set.
the global MAE (which takes all the sentences into account) increases from 0.7 (0%) to 0.9 (50% of the post-edited sentences added): since the system assigns more scores in the top tail, it makes larger errors globally.