  The Natural Language Inference (NLI) task often requires reasoning over
multiple steps to reach the conclusion. While the necessity of generating such
intermediate steps (instead of a summary explanation) has gained popular
support, it is unclear how to generate such steps without complete end-to-end
supervision and how such generated steps can be further utilized. In this work,
we train a sequence-to-sequence model to generate only the next step given an
NLI premise and hypothesis pair (and previous steps); then enhance it with
external knowledge and symbolic search to generate intermediate steps with only
next-step supervision. We show the correctness of such generated steps through
automated and human verification. Furthermore, we show that such generated
steps can help improve end-to-end NLI task performance using simple data
augmentation strategies, across multiple public NLI datasets.
