One possible reason for this is that during the course of learning, the model will see many segments that are nearly correct, e.g., segments that overlap correct segments and differ by a single token.
the model learns to score all segments containing even a small amount of negative evidence as invalid in order to ensure that these nearly correct segments have a sufficiently low score.