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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–209 of 209 papers

TitleStatusHype
Continuous Decomposition of Granularity for Neural Paraphrase GenerationCode0
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model RobustnessCode0
SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucinationCode0
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase GenerationCode0
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-TranslationCode0
SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text GenerationCode0
Sound Natural: Content Rephrasing in Dialog SystemsCode0
Building a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation SystemsCode0
Retrieve, Generate, Evaluate: A Case Study for Medical Paraphrases Generation with Small Language ModelsCode0
Show:102550
← PrevPage 5 of 5Next →
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