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 126–150 of 209 papers

TitleStatusHype
Unsupervised Paraphrase Generation using Pre-trained Language Models—0
Unsupervised Paraphrasing with Pretrained Language Models—0
Unsupervised Paraphrasing by Simulated Annealing—0
Unsupervised Paraphrasing via Deep Reinforcement Learning—0
Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations—0
Unsupervised Text Generation by Learning from Search—0
Using character overlap to improve language transformation—0
Using Paraphrases and Lexical Semantics to Improve the Accuracy and the Robustness of Supervised Models in Situated Dialogue Systems—0
Vector-Quantized Prompt Learning for Paraphrase Generation—0
Controllable Paraphrase Generation with a Syntactic Exemplar—0
Zero-Shot Paraphrase Generation with Multilingual Language Models—0
Polly Want a Cracker: Analyzing Performance of Parroting on Paraphrase Generation Datasets—0
A Comparison of Two Paraphrase Models for Taxonomy Augmentation—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment—0
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation—0
An End-to-End Generative Architecture for Paraphrase Generation—0
A Paraphrase Generation System for EHR Question Answering—0
Are We Evaluating Paraphrase Generation Accurately?—0
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation—0
A task in a suit and a tie: paraphrase generation with semantic augmentation—0
Automatically Ranked Russian Paraphrase Corpus for Text Generation—0
Automatic and Human-AI Interactive Text Generation—0
CogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks—0
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising—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