# A Deep Generative Framework for Paraphrase Generation

arXiv: https://arxiv.org/abs/1709.05074 · HTML: https://mlsanity.com/paper/1709.05074/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2017.

## Built on by (most cited first, 30 of 54)

- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Latent Diffusion Energy-Based Model for Interpretable Text Modeling](https://mlsanity.com/paper/2206.05895/) (2022)
- [Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs](https://mlsanity.com/paper/2101.10579/) (2021)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Quality Controlled Paraphrase Generation](https://mlsanity.com/paper/2203.10940/) (2022)
- [ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation](https://mlsanity.com/paper/2101.08382/) (2021)
- [Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning](https://mlsanity.com/paper/2202.00535/) (2022)
- [Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information](https://mlsanity.com/paper/2302.05096/) (2023)
- [Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection](https://mlsanity.com/paper/2106.04681/) (2021)
- [Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation](https://mlsanity.com/paper/2207.06130/) (2022)
- [Improving Paraphrase Detection with the Adversarial Paraphrasing Task](https://mlsanity.com/paper/2106.07691/) (2021)
- [Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts](https://mlsanity.com/paper/2203.07285/) (2022)
- [Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning](https://mlsanity.com/paper/2404.16807/) (2024)
- [A Review of Text Style Transfer using Deep Learning](https://mlsanity.com/paper/2109.15144/) (2021)
- [Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification](https://mlsanity.com/paper/2108.11556/) (2021)
- [ConRPG: Paraphrase Generation using Contexts as Regularizer](https://mlsanity.com/paper/2109.00363/) (2021)
- [Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering](https://mlsanity.com/paper/2109.07095/) (2021)
- [ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation](https://mlsanity.com/paper/2305.16585/) (2023)
- [Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning](https://mlsanity.com/paper/2103.12777/) (2021)
- [Multi-objective Deep Data Generation with Correlated Property Control](https://mlsanity.com/paper/2210.01796/) (2022)
- [Prophet Attention: Predicting Attention with Future Attention for Image Captioning](https://mlsanity.com/paper/2210.10914/) (2022)
- [Monotonic Paraphrasing Improves Generalization of Language Model Prompting](https://mlsanity.com/paper/2403.16038/) (2024)
- [Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach](https://mlsanity.com/paper/2109.01862/) (2021)
- [Improving Non-autoregressive Generation with Mixup Training](https://mlsanity.com/paper/2110.11115/) (2021)
- [Idiomatic Expression Paraphrasing without Strong Supervision](https://mlsanity.com/paper/2112.08592/) (2021)
- [A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMs](https://mlsanity.com/paper/2404.00303/) (2024)
- [Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation](https://mlsanity.com/paper/2109.01484/) (2021)
- [From Solving a Problem Boldly to Cutting the Gordian Knot: Idiomatic Text Generation](https://mlsanity.com/paper/2104.06541/) (2021)
- [Simulated annealing for optimization of graphs and sequences](https://mlsanity.com/paper/2110.01384/) (2021)
- [BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust Machine Translation Evaluation](https://mlsanity.com/paper/2305.19144/) (2023)

## Abstract (arXiv)

Paraphrase generation is an important problem in NLP, especially in question answering, information retrieval, information extraction, conversation systems, to name a few. In this paper, we address the problem of generating paraphrases automatically. Our proposed method is based on a combination of deep generative models (VAE) with sequence-to-sequence models (LSTM) to generate paraphrases, given an input sentence. Traditional VAEs when combined with recurrent neural networks can generate free text but they are not suitable for paraphrase generation for a given sentence. We address this problem by conditioning the both, encoder and decoder sides of VAE, on the original sentence, so that it can generate the given sentence's paraphrases. Unlike most existing models, our model is simple, modular and can generate multiple paraphrases, for a given sentence. Quantitative evaluation of the proposed method on a benchmark paraphrase dataset demonstrates its efficacy, and its performance improvement over the state-of-the-art methods by a significant margin, whereas qualitative human evaluation indicate that the generated paraphrases are well-formed, grammatically correct, and are relevant to the input sentence. Furthermore, we evaluate our method on a newly released question paraphrase dataset, and establish a new baseline for future research.