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Text Simplification On Newsela

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このベンチマークにおける各モデルのパフォーマンス結果

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