Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation

Cyril Chhun, Pierre Colombo, Fabian M. Suchanek, Chloé Clavel


Abstract
Research on Automatic Story Generation (ASG) relies heavily on human and automatic evaluation. However, there is no consensus on which human evaluation criteria to use, and no analysis of how well automatic criteria correlate with them. In this paper, we propose to re-evaluate ASG evaluation. We introduce a set of 6 orthogonal and comprehensive human criteria, carefully motivated by the social sciences literature. We also present HANNA, an annotated dataset of 1,056 stories produced by 10 different ASG systems. HANNA allows us to quantitatively evaluate the correlations of 72 automatic metrics with human criteria. Our analysis highlights the weaknesses of current metrics for ASG and allows us to formulate practical recommendations for ASG evaluation.
Anthology ID:
2022.coling-1.509
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5794–5836
Language:
URL:
https://aclanthology.org/2022.coling-1.509
DOI:
Bibkey:
Cite (ACL):
Cyril Chhun, Pierre Colombo, Fabian M. Suchanek, and Chloé Clavel. 2022. Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5794–5836, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation (Chhun et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.509.pdf
Code
 dig-team/hanna-benchmark-asg +  additional community code
Data
HANNAWritingPrompts