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 51–75 of 209 papers

TitleStatusHype
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
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
Emotion and Sentiment Guided Paraphrasing—0
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-TranslationCode0
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing—0
PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase GenerationCode0
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model RobustnessCode0
Lost in Translationese? Reducing Translation Effect Using Abstract Meaning RepresentationCode0
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
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
Learning Structural Information for Syntax-Controlled Paraphrase Generation—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