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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 1–50 of 209 papers

TitleStatusHype
Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked DataCode0
A Large-Scale Benchmark for Vietnamese Sentence ParaphrasesCode0
SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text GenerationCode0
Learning to Adapt to Low-Resource Paraphrase Generation—0
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language ModelsCode0
Multi-Attribute Linguistic Tuning for Controlled Paraphrase Generation—0
Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language ModelsCode0
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment—0
Action Controlled ParaphrasingCode0
Parameter Efficient Diverse Paraphrase Generation Using Sequence-Level Knowledge Distillation—0
ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused with High-Quality Lexical and Syntactic Diversity—0
Improved Paraphrase Generation via Controllable Latent DiffusionCode0
SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucinationCode0
SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes—0
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing—0
Neural Machine Translation for Malayalam Paraphrase Generation—0
Vector-Quantized Prompt Learning for Paraphrase Generation—0
Paraphrase Types for Generation and DetectionCode1
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
Contextual Data Augmentation for Task-Oriented Dialog Systems—0
Automatic and Human-AI Interactive Text Generation—0
Multilingual Lexical Simplification via Paraphrase GenerationCode0
Explicit Syntactic Guidance for Neural Text GenerationCode1
Emotion and Sentiment Guided Paraphrasing—0
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-TranslationCode0
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