Due to this, the different entailed hypotheses of the same premise land up in different splits of the dataset (e.g., one in train and one in test/validation) in many cases.
we regroup the premiseentailment pairs and modify the split as follows: among the 190, 113 premise-entailment pairs subset of the SNLI corpus, there are 155, 898 unique premises; out of which 145, 822 have only one hy-pothesis and we make this the training set, and the rest of them (10, 076) have more than one hypothesis, which we randomly shuffle and divide equally into test and validation sets, so that each of these two sets has approximately the same distribution of the number of reference hypotheses per premise.