  We introduce a new large-scale NLI benchmark dataset, collected via an
iterative, adversarial human-and-model-in-the-loop procedure. We show that
training models on this new dataset leads to state-of-the-art performance on a
variety of popular NLI benchmarks, while posing a more difficult challenge with
its new test set. Our analysis sheds light on the shortcomings of current
state-of-the-art models, and shows that non-expert annotators are successful at
finding their weaknesses. The data collection method can be applied in a
never-ending learning scenario, becoming a moving target for NLU, rather than a
static benchmark that will quickly saturate.
