  Existing datasets for natural language inference (NLI) have propelled
research on language understanding. We propose a new method for automatically
deriving NLI datasets from the growing abundance of large-scale question
answering datasets. Our approach hinges on learning a sentence transformation
model which converts question-answer pairs into their declarative forms.
Despite being primarily trained on a single QA dataset, we show that it can be
successfully applied to a variety of other QA resources. Using this system, we
automatically derive a new freely available dataset of over 500k NLI examples
(QA-NLI), and show that it exhibits a wide range of inference phenomena rarely
seen in previous NLI datasets.
