A model of y with access only to the hypothesis side can fit the data perfectly by detecting the presence or absence of c in H, ignoring the more general pattern.
we hypothesize that a model that learns p θ (y | P, H) by training on such data would be misled by the bias c and would fail to learn the relationship between the premise and the hypothesis.