Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification

Liam Cripwell, Joël Legrand, Claire Gardent


Abstract
Automatic evaluation for sentence simplification remains a challenging problem. Most popular evaluation metrics require multiple high-quality references – something not readily available for simplification – which makes it difficult to test performance on unseen domains. Furthermore, most existing metrics conflate simplicity with correlated attributes such as fluency or meaning preservation. We propose a new learned evaluation metric — SLE — which focuses on simplicity, outperforming almost all existing metrics in terms of correlation with human judgements.
Anthology ID:
2023.emnlp-main.739
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12053–12059
Language:
URL:
https://aclanthology.org/2023.emnlp-main.739
DOI:
10.18653/v1/2023.emnlp-main.739
Bibkey:
Cite (ACL):
Liam Cripwell, Joël Legrand, and Claire Gardent. 2023. Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12053–12059, Singapore. Association for Computational Linguistics.
Cite (Informal):
Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification (Cripwell et al., EMNLP 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.emnlp-main.739.pdf
Video:
 https://aclanthology.org/2023.emnlp-main.739.mp4