SOTAVerified

Paraphrase Generation

Paraphrase Generation involves transforming a natural language sentence to a new sentence, that has the same semantic meaning but a different syntactic or lexical surface form.

Papers

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Showing 26–50 of 209 papers

TitleStatusHype
PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase GenerationCode0
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing—0
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model RobustnessCode0
CoEdIT: Text Editing by Task-Specific Instruction TuningCode1
TESS: Text-to-Text Self-Conditioned Simplex DiffusionCode1
Lost in Translationese? Reducing Translation Effect Using Abstract Meaning RepresentationCode0
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defenseCode1
kNN-BOX: A Unified Framework for Nearest Neighbor GenerationCode1
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising—0
Syntactically Robust Training on Partially-Observed Data for Open Information ExtractionCode0
Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generationCode0
Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations—0
Fine-Grained Emotional Paraphrasing along Emotion Gradients—0
Revision for Concision: A Constrained Paraphrase Generation Task—0
Metaphorical Paraphrase Generation: Feeding Metaphorical Language Models with Literal Texts—0
How Large Language Models are Transforming Machine-Paraphrased PlagiarismCode1
Improving Large-scale Paraphrase Acquisition and Generation—0
Continuous Decomposition of Granularity for Neural Paraphrase GenerationCode0
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data AugmentationCode0
Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning—0
Unsupervised Paraphrasability Prediction for Compound Nominalizations—0
Learning Structural Information for Syntax-Controlled Paraphrase Generation—0
'John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word ProblemsCode0
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer—0
Investigating the use of Paraphrase Generation for Question Reformulation in the FRANK QA system—0
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All datasetsParalexQuora Question PairsMSCOCO

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HRQ-VAEiBLEU24.93—Unverified
2SeparatoriBLEU14.84—Unverified
#ModelMetricClaimedVerifiedStatus
1HRQ-VAEiBLEU18.42—Unverified
2SeparatoriBLEU5.84—Unverified
#ModelMetricClaimedVerifiedStatus
1HRQ-VAEBLEU27.9—Unverified