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
Edit Distance Based Curriculum Learning for Paraphrase Generation—0
Neural semi-Markov CRF for Monolingual Word AlignmentCode1
Factorising Meaning and Form for Intent-Preserving ParaphrasingCode1
Neural-Driven Search-Based Paraphrase Generation—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel PairsCode1
ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase GenerationCode1
Delexicalized 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
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
Latent Template Induction with Gumbel-CRFsCode1
Generative Pre-training for Paraphrase Generation by Representing and Predicting Spans in Exemplars—0
Sound Natural: Content Rephrasing in Dialog SystemsCode0
Paraphrase Generation via Adversarial Penalizations—0
DivGAN: Towards Diverse Paraphrase Generation via Diversified Generative Adversarial Network—0
Gradient-guided Unsupervised Lexically Constrained Text Generation—0
Unsupervised Paraphrasing with Pretrained Language Models—0
Neural-Driven Multi-criteria Tree Search for Paraphrase Generation—0
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention—0
Reformulating Unsupervised Style Transfer as Paraphrase GenerationCode1
Exemplar-Controllable Paraphrasing and Translation using BitextCode0
iNLTK: Natural Language Toolkit for Indic LanguagesCode1
Don't Change Me! User-Controllable Selective 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