Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA

David Heineman, Yao Dou, Mounica Maddela, Wei Xu


Abstract
Large language models (e.g., GPT-4) are uniquely capable of producing highly rated text simplification, yet current human evaluation methods fail to provide a clear understanding of systems’ specific strengths and weaknesses. To address this limitation, we introduce SALSA, an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation. We develop twenty one linguistically grounded edit types, covering the full spectrum of success and failure across dimensions of conceptual, syntactic and lexical simplicity. Using SALSA, we collect 19K edit annotations on 840 simplifications, revealing discrepancies in the distribution of simplification strategies performed by fine-tuned models, prompted LLMs and humans, and find GPT-3.5 performs more quality edits than humans, but still exhibits frequent errors. Using our fine-grained annotations, we develop LENS-SALSA, a reference-free automatic simplification metric, trained to predict sentence- and word-level quality simultaneously. Additionally, we introduce word-level quality estimation for simplification and report promising baseline results. Our data, new metric, and annotation toolkit are available at https://salsa-eval.com.
Anthology ID:
2023.emnlp-main.211
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:
3466–3495
Language:
URL:
https://aclanthology.org/2023.emnlp-main.211
DOI:
10.18653/v1/2023.emnlp-main.211
Bibkey:
Cite (ACL):
David Heineman, Yao Dou, Mounica Maddela, and Wei Xu. 2023. Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3466–3495, Singapore. Association for Computational Linguistics.
Cite (Informal):
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA (Heineman et al., EMNLP 2023)
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PDF:
https://aclanthology.org/2023.emnlp-main.211.pdf