Abstract
Most Natural Language Generation systems need to produce accurate texts. We propose a methodology for high-quality human evaluation of the accuracy of generated texts, which is intended to serve as a gold-standard for accuracy evaluations of data-to-text systems. We use our methodology to evaluate the accuracy of computer generated basketball summaries. We then show how our gold standard evaluation can be used to validate automated metrics.- Anthology ID:
- 2020.inlg-1.22
- Volume:
- Proceedings of the 13th International Conference on Natural Language Generation
- Month:
- December
- Year:
- 2020
- Address:
- Dublin, Ireland
- Editors:
- Brian Davis, Yvette Graham, John Kelleher, Yaji Sripada
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 158–168
- Language:
- URL:
- https://aclanthology.org/2020.inlg-1.22
- DOI:
- 10.18653/v1/2020.inlg-1.22
- Cite (ACL):
- Craig Thomson and Ehud Reiter. 2020. A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems. In Proceedings of the 13th International Conference on Natural Language Generation, pages 158–168, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systems (Thomson & Reiter, INLG 2020)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2020.inlg-1.22.pdf
- Code
- nlgcat/evaluating_accuracy