Generation Challenges: Results of the Accuracy Evaluation Shared Task

Craig Thomson, Ehud Reiter


Abstract
The Shared Task on Evaluating Accuracy focused on techniques (both manual and automatic) for evaluating the factual accuracy of texts produced by neural NLG systems, in a sports-reporting domain. Four teams submitted evaluation techniques for this task, using very different approaches and techniques. The best-performing submissions did encouragingly well at this difficult task. However, all automatic submissions struggled to detect factual errors which are semantically or pragmatically complex (for example, based on incorrect computation or inference).
Anthology ID:
2021.inlg-1.23
Volume:
Proceedings of the 14th International Conference on Natural Language Generation
Month:
August
Year:
2021
Address:
Aberdeen, Scotland, UK
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
240–248
Language:
URL:
https://aclanthology.org/2021.inlg-1.23
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.inlg-1.23.pdf