  Natural Language Inference is an important task for Natural Language
Understanding. It is concerned with classifying the logical relation between
two sentences. In this paper, we propose several text generative neural
networks for generating text hypothesis, which allows construction of new
Natural Language Inference datasets. To evaluate the models, we propose a new
metric -- the accuracy of the classifier trained on the generated dataset. The
accuracy obtained by our best generative model is only 2.7% lower than the
accuracy of the classifier trained on the original, human crafted dataset.
Furthermore, the best generated dataset combined with the original dataset
achieves the highest accuracy. The best model learns a mapping embedding for
each training example. By comparing various metrics we show that datasets that
obtain higher ROUGE or METEOR scores do not necessarily yield higher
classification accuracies. We also provide analysis of what are the
characteristics of a good dataset including the distinguishability of the
generated datasets from the original one.
