Title :

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

Author(s) :

Cripwell, Liam [Auteur]
Natural Language Processing : representations, inference and semantics [SYNALP]
Legrand, Joël [Auteur]
Natural Language Processing : representations, inference and semantics [SYNALP]
Machine Learning in Information Networks [MAGNET]
Gardent, Claire [Auteur]
Natural Language Processing : representations, inference and semantics [SYNALP]

Conference title :

2023 Conference on Empirical Methods in Natural Language Processing

City :

Singapore

Country :

Singapour

Start date of the conference :

2023-12-06

Book title :

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Publisher :

Association for Computational Linguistics

HAL domain(s) :

Informatique [cs]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL