Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification
Cripwell, Liam, Legrand, Joël, Gardent, Claire
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Oct-12-2023
- Country:
- Asia
- Europe
- Croatia > Dubrovnik-Neretva County
- Dubrovnik (0.04)
- France > Provence-Alpes-Côte d'Azur
- Bouches-du-Rhône > Marseille (0.04)
- Ireland > Leinster
- County Dublin > Dublin (0.05)
- Netherlands (0.04)
- Sweden > Vaestra Goetaland
- Gothenburg (0.04)
- Croatia > Dubrovnik-Neretva County
- North America
- Canada
- British Columbia > Metro Vancouver Regional District
- Vancouver (0.04)
- Ontario > Toronto (0.04)
- British Columbia > Metro Vancouver Regional District
- Dominican Republic (0.04)
- United States
- Louisiana > Orleans Parish
- New Orleans (0.04)
- Washington > King County
- Seattle (0.04)
- Louisiana > Orleans Parish
- Canada
- Genre:
- Research Report (0.82)
- Technology: