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.- Anthology ID:
- 2022.naacl-main.350
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4753–4763
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.350
- DOI:
- 10.18653/v1/2022.naacl-main.350
- Cite (ACL):
- Neha Srikanth and Rachel Rudinger. 2022. Partial-input baselines show that NLI models can ignore context, but they don’t.. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4753–4763, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Partial-input baselines show that NLI models can ignore context, but they don’t. (Srikanth & Rudinger, NAACL 2022)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2022.naacl-main.350.pdf
- Code
- nehasrikn/context-editing
- Data
- SNLI