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 176–200 of 209 papers

TitleStatusHype
Metaphor: A Computational Perspective by Tony Veale, Ekaterina Shutova and Beata Beigman Klebanov—0
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations—0
Paraphrase Generation with Deep Reinforcement Learning—0
A Deep Generative Framework for Paraphrase GenerationCode1
Neural Paraphrase Generation using Transfer Learning—0
Building a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation SystemsCode0
Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection—0
CogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks—0
The CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations—0
Neural Clinical Paraphrase Generation with Attention—0
Neural Paraphrase Generation with Stacked Residual LSTM NetworksCode1
Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement—0
Contextual LSTM (CLSTM) models for Large scale NLP tasks—0
Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing—0
Polarity Classification of Short Product Reviews via Multiple Cluster-based SVM Classifiers—0
Sentential Paraphrase Generation for Agglutinative Languages Using SVM with a String Kernel—0
Crowdsourcing Language Generation Templates for Dialogue Systems—0
Modelling Events through Memory-based, Open-IE Patterns for Abstractive Summarization—0
How to make words with vectors: Phrase generation in distributional semantics—0
The Multilingual Paraphrase Database—0
Creating and using large monolingual parallel corpora for sentential paraphrase generation—0
Supervised Sentence Fusion with Single-Stage Inference—0
Using Paraphrases and Lexical Semantics to Improve the Accuracy and the Robustness of Supervised Models in Situated Dialogue Systems—0
Leveraging Crowdsourcing for Paraphrase Recognition—0
Using character overlap to improve language transformation—0
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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