@inproceedings{srikanth-rudinger-2022-partial,
title = "Partial-input baselines show that {NLI} models can ignore context, but they don{'}t.",
author = "Srikanth, Neha and
Rudinger, Rachel",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.350",
doi = "10.18653/v1/2022.naacl-main.350",
pages = "4753--4763",
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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%0 Conference Proceedings
%T Partial-input baselines show that NLI models can ignore context, but they don’t.
%A Srikanth, Neha
%A Rudinger, Rachel
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F srikanth-rudinger-2022-partial
%X 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.
%R 10.18653/v1/2022.naacl-main.350
%U https://aclanthology.org/2022.naacl-main.350
%U https://doi.org/10.18653/v1/2022.naacl-main.350
%P 4753-4763
Markdown (Informal)
[Partial-input baselines show that NLI models can ignore context, but they don’t.](https://aclanthology.org/2022.naacl-main.350) (Srikanth & Rudinger, NAACL 2022)
ACL