We hypothesize this is due to the issue of a disconnect between training and test-time objectives.
the system is trained solely to raise likelihood of gold-standard extractions, and during training the model is not aware of its test-time behavior of ranking a set of system-generated assertions across sentences that potentially include incorrect extractions.