HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation

Qianyu He, Yikai Zhang, Jiaqing Liang, Yuncheng Huang, Yanghua Xiao, Yunwen Chen


Abstract
Similes play an imperative role in creative writing such as story and dialogue generation. Proper evaluation metrics are like a beacon guiding the research of simile generation (SG). However, it remains under-explored as to what criteria should be considered, how to quantify each criterion into metrics, and whether the metrics are effective for comprehensive, efficient, and reliable SG evaluation. To address the issues, we establish HAUSER, a holistic and automatic evaluation system for the SG task, which consists of five criteria from three perspectives and automatic metrics for each criterion. Through extensive experiments, we verify that our metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics. Resources of HAUSER are publicly available at https://github.com/Abbey4799/HAUSER.
Anthology ID:
2023.acl-long.702
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long 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:
12557–12572
Language:
URL:
https://aclanthology.org/2023.acl-long.702
DOI:
10.18653/v1/2023.acl-long.702
Bibkey:
Cite (ACL):
Qianyu He, Yikai Zhang, Jiaqing Liang, Yuncheng Huang, Yanghua Xiao, and Yunwen Chen. 2023. HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12557–12572, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation (He et al., ACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.acl-long.702.pdf
Video:
 https://aclanthology.org/2023.acl-long.702.mp4