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

Neha Srikanth; Rachel Rudinger

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arXiv · 2205.12181

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

Neha Srikanth·Rachel Rudinger

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Abstract

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.

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Neha Srikanth, Rachel Rudinger. 2022-05-24. Partial-input baselines show that NLI models can ignore context, but they don't. https://arxiv.org/abs/2205.12181

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