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.
Cite
CITATION STYLE
Cripwell, L., Legrand, J., & Gardent, C. (2023). Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification. In EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 12053–12059). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2023.emnlp-main.739
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