  The task of natural language inference (NLI) is to identify the relation
between the given premise and hypothesis. While recent NLI models achieve very
high performance on individual datasets, they fail to generalize across similar
datasets. This indicates that they are solving NLI datasets instead of the task
itself. In order to improve generalization, we propose to extend the input
representations with an abstract view of the relation between the hypothesis
and the premise, i.e., how well the individual words, or word n-grams, of the
hypothesis are covered by the premise. Our experiments show that the use of
this information considerably improves generalization across different NLI
datasets without requiring any external knowledge or additional data. Finally,
we show that using the coverage information is not only beneficial for
improving the performance across different datasets of the same task. The
resulting generalization improves the performance across datasets that belong
to similar but not the same tasks.
