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 101–125 of 209 papers

TitleStatusHype
Revisiting Pivot-Based Paraphrase Generation: Language Is Not the Only Optional Pivot—0
Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems—0
Exploring Metaphoric Paraphrase GenerationCode0
Ask me in your own words: paraphrasing for multitask question answeringCode0
Improving the Diversity of Unsupervised Paraphrasing with Embedding OutputsCode0
Improving Non-autoregressive Generation with Mixup TrainingCode0
GCPG: A General Framework for Controllable Paraphrase Generation—0
Simulated annealing for optimization of graphs and sequences—0
Discovering Latent Network Topology in Contextualized Representations with Randomized Dynamic Programming—0
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation—0
Towards Better Characterization of ParaphrasesCode0
Paraphrase Generation as Unsupervised Machine Translation—0
ConRPG: Paraphrase Generation using Contexts as Regularizer—0
SPMoE: Generate Multiple Pattern-Aware Outputs with Sparse Pattern Mixture of Experts—0
Keep the Primary, Rewrite the Secondary: A Two-Stage Approach for Paraphrase Generation—0
Edit Distance Based Curriculum Learning for Paraphrase Generation—0
Neural-Driven Search-Based Paraphrase Generation—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
Delexicalized Paraphrase Generation—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
A Semantically Consistent and Syntactically Variational Encoder-Decoder Framework for Paraphrase Generation—0
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems—0
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation—0
Generative Pre-training for Paraphrase Generation by Representing and Predicting Spans in Exemplars—0
Sound Natural: Content Rephrasing in Dialog SystemsCode0
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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