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When strong partial-input baselines reveal artifacts in crowdsourced NLIdatasets, the performance of full-input models trained on such datasets isoften dismissed as reliance on spurious correlations. We investigate whetherstate-of-the-art NLI models are capable of overriding default inferences madeby a partial-input baseline. We introduce an evaluation set of 600 examplesconsisting of perturbed premises to examine a RoBERTa model's sensitivity toedited contexts. Our results indicate that NLI models are still capable oflearning to condition on context--a necessary component of inferentialreasoning--despite being trained on artifact-ridden datasets.

Partial-input baselines show that NLI models can ignore context, but they don't