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Text Simplification On Newsela

Metrics

BLEU
SARI

Results

Performance results of various models on this benchmark

Model Name
BLEU
SARI
Paper TitleRepository
DRESS23.2127.37Sentence Simplification with Deep Reinforcement Learning-
Edit-Unsup-TS17.3630.44Iterative Edit-Based Unsupervised Sentence Simplification-
Pointer + Multi-task Entailment and Paraphrase Generation11.1433.22Dynamic Multi-Level Multi-Task Learning for Sentence Simplification-
NSELSTM-B 26.3127.42Sentence Simplification with Memory-Augmented Neural Networks-
S2S-Cluster-FA19.5530.73Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification-
Hybrid 14.46*28.61*--
NSELSTM-S 22.6229.58Sentence Simplification with Memory-Augmented Neural Networks-
DRESS-LS24.3026.63Sentence Simplification with Deep Reinforcement Learning-
DMASS + DCSS-27.28Integrating Transformer and Paraphrase Rules for Sentence Simplification-
CRF Alignment + Transformer-36.6Neural CRF Model for Sentence Alignment in Text Simplification-
EditNTS19.8531.41EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing-
SeqLabel -29.53*Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs
PBMT-R 18.19*15.77*--
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Text Simplification On Newsela | SOTA | HyperAI