  When strong partial-input baselines reveal artifacts in crowdsourced NLI
datasets, the performance of full-input models trained on such datasets is
often dismissed as reliance on spurious correlations. We investigate whether
state-of-the-art NLI models are capable of overriding default inferences made
by a partial-input baseline. We introduce an evaluation set of 600 examples
consisting of perturbed premises to examine a RoBERTa model's sensitivity to
edited contexts. Our results indicate that NLI models are still capable of
learning to condition on context--a necessary component of inferential
reasoning--despite being trained on artifact-ridden datasets.
