Partial-input baselines show that NLI models can ignore context, but they don’t.
Neha Pundlik Srikanth, Rachel Rudinger · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
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 stateof-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.