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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 26–50 of 209 papers

TitleStatusHype
CoEdIT: Text Editing by Task-Specific Instruction TuningCode1
Paraphrase Types for Generation and DetectionCode1
Neural Syntactic Preordering for Controlled Paraphrase GenerationCode1
Paraphrase Generation with Latent Bag of WordsCode1
Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic DiversityCode1
A large-scale computational study of content preservation measures for text style transfer and paraphrase generationCode1
Factorising Meaning and Form for Intent-Preserving ParaphrasingCode1
Explicit Syntactic Guidance for Neural Text GenerationCode1
ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase GenerationCode1
Contrastive Representation Learning for Exemplar-Guided Paraphrase GenerationCode1
Quality Controlled Paraphrase GenerationCode1
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
A Large-Scale Benchmark for Vietnamese Sentence ParaphrasesCode0
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-TranslationCode0
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model RobustnessCode0
Discriminating between Lexico-Semantic Relations with the Specialization Tensor ModelCode0
Building a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation SystemsCode0
Multilingual Lexical Simplification via Paraphrase GenerationCode0
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language ModelsCode0
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase GenerationCode0
Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generationCode0
Improving the Diversity of Unsupervised Paraphrasing with Embedding OutputsCode0
Improving Non-autoregressive Generation with Mixup TrainingCode0
'John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word ProblemsCode0
Learning Semantic Sentence Embeddings using Sequential Pair-wise DiscriminatorCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HRQ-VAEiBLEU24.93—Unverified
2SeparatoriBLEU14.84—Unverified
#ModelMetricClaimedVerifiedStatus
1HRQ-VAEiBLEU18.42—Unverified
2SeparatoriBLEU5.84—Unverified
#ModelMetricClaimedVerifiedStatus
1HRQ-VAEBLEU27.9—Unverified