Text Simplification On Turkcorpus

Metrics

BLEU

FKGL

SARI (EASSEu003e=0.2.1)

Results

Performance results of various models on this benchmark

				Paper Title	Repository
GPT-175B (6 SARI-selected examples, high/low)	79.83	9.33	43.46	Metric-Based In-context Learning: A Case Study in Text Simplification
MUSS (BART+ACCESS Supervised)	78.17	7.60	42.53	MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases
Control Prefixes (BART)	-	7.74	42.32	Control Prefixes for Parameter-Efficient Text Generation
TST	-	-	41.46	Text Simplification by Tagging
ACCESS	72.53	-	41.38	Controllable Sentence Simplification
MUSS (BART+ACCESS Unsupervised)	-	8.79	40.85	MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases
DMASS-DCSS	-	-	40.45	Integrating Transformer and Paraphrase Rules for Sentence Simplification
SBMT-SARI	73.08*	-	39.56	Optimizing Statistical Machine Translation for Text Simplification	-
EditNTS	86.69	-	38.22	EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
PBMT-R	-	-	38.04	-	-
Edit-Unsup-TS	73.62	-	37.85	Iterative Edit-Based Unsupervised Sentence Simplification
Pointer + Multi-task Entailment and Paraphrase Generation	81.49	-	37.45	Dynamic Multi-Level Multi-Task Learning for Sentence Simplification	-
Dress-LS	80.12	-	37.27	Sentence Simplification with Deep Reinforcement Learning
NTS-SARI	80.69	-	37.25	Exploring Neural Text Simplification Models	-
UNMT (Unsupervised)	74.02	-	37.20	Unsupervised Neural Text Simplification
UNTS-10k (Weakly supervised)	-	-	37.15	Unsupervised Neural Text Simplification
Dress	77.18	-	37.08	Sentence Simplification with Deep Reinforcement Learning
SeqLabel	-	-	37.08*	Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs	-
NSELSTM-S	80.43	-	36.88	Sentence Simplification with Memory-Augmented Neural Networks	-
SEMoses	74.49	-	36.70	Simple and Effective Text Simplification Using Semantic and Neural Methods	-

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Text Simplification On Turkcorpus

Metrics

Results