Paraphrase Generation
69 papers with code • 3 benchmarks • 16 datasets
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.
Datasets
MS COCO
MSCOCO
Quora Question Pairs
PARANMT-50M
Duolingo STAPLE Shared Task
TextBox 2.0
Most implemented papers
Hierarchical Sketch Induction for Paraphrase Generation
tomhosking/hrq-vae
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ACL 2022
We propose a generative model of paraphrase generation, that encourages syntactic diversity by conditioning on an explicit syntactic sketch.
Quality Controlled Paraphrase Generation
ibm/quality-controlled-paraphrase-generation
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ACL 2022
Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases.
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors
wyu-du/gp-vae
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The proposed stochastic function is sampled from a Gaussian process prior to (1) provide infinite number of joint Gaussian distributions of random context variables (diversity-promoting) and (2) explicitly model dependency between context variables (accurate-encoding).
Chinese Idiom Paraphrasing
jpqiang/chinese-idiom-paraphrasing
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Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters.
Principled Paraphrase Generation with Parallel Corpora
aitorormazabal/paraphrasing-from-parallel
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ACL 2022
Round-trip Machine Translation (MT) is a popular choice for paraphrase generation, which leverages readily available parallel corpora for supervision.
Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian
The first task is framed as a binary classification problem.
'John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems
There is a need for paraphrase scoring methods in the context of AWP to enable the training of good paraphrasers.
PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation
hongyuanluke/pcc
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This paper presents \textbf{PCC}: \textbf{P}araphrasing with Bottom-k Sampling and \textbf{C}yclic Learning for \textbf{C}urriculum Data Augmentation, a novel CDA framework via paraphrasing, which exploits the textual paraphrase similarity as the curriculum difficulty measure.
Continuous Decomposition of Granularity for Neural Paraphrase Generation
While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information.
Language as a Latent Sequence: deep latent variable models for semi-supervised paraphrase generation
jialin-yu/latent-sequence-paraphrase
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To leverage information from text pairs, we additionally introduce a novel supervised model we call dual directional learning (DDL), which is designed to integrate with our proposed VSAR model.
