Abstract
This work examines the use of contradiction in natural language inference (NLI) for question answering (QA). Typically, NLI systems help answer questions by determining if a potential answer is entailed (supported) by some background context. But is it useful to also determine if an answer contradicts the context? We test this in two settings, multiple choice and extractive QA, and find that systems that incorporate contradiction can do slightly better than entailment-only systems on certain datasets. However, the best performances come from using contradiction, entailment, and QA model confidence scores together. This has implications for the deployment of QA systems in domains such as medicine and science where safety is an issue.- Anthology ID:
- 2023.acl-short.72
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 827–840
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.72
- DOI:
- 10.18653/v1/2023.acl-short.72
- Cite (ACL):
- Etienne Fortier-Dubois and Domenic Rosati. 2023. Using contradictions improves question answering systems. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 827–840, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Using contradictions improves question answering systems (Fortier-Dubois & Rosati, ACL 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.acl-short.72.pdf