Our findings, together with the previous negative findings e.g. by <cite>Glockner et al. (2018)</cite> and Gururangan et al. (2018) , indicate that the current state-of-the-art neural network models fail to capture the semantics of NLI in a way that will enable them to generalize across different NLI situations. The results indicate two issues to be taken into consideration: a) using datasets involving a fraction of what NLI is, will fail when tested in datasets that are testing for a slightly different definition.