# D-PAGE: Diverse Paraphrase Generation

arXiv: https://arxiv.org/abs/1808.04364 · HTML: https://mlsanity.com/paper/1808.04364/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning](https://mlsanity.com/paper/2106.01354/) (2021)
- [ConRPG: Paraphrase Generation using Contexts as Regularizer](https://mlsanity.com/paper/2109.00363/) (2021)
- [Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors](https://mlsanity.com/paper/2204.01227/) (2022)
- [Towards Effective Paraphrasing for Information Disguise](https://mlsanity.com/paper/2311.05018/) (2023)
- [SPMoE: Generate Multiple Pattern-Aware Outputs with Sparse Pattern Mixture of Experts](https://mlsanity.com/paper/2108.07535/) (2021)
- [Paraphrase Generation as Unsupervised Machine Translation](https://mlsanity.com/paper/2109.02950/) (2021)
- [IdEALS: Idiomatic Expressions for Advancement of Language Skills](https://mlsanity.com/paper/2305.13637/) (2023)

## Abstract (arXiv)

In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs. We propose a simple method Diverse Paraphrase Generation (D-PAGE), which extends neural machine translation (NMT) models to support the generation of diverse paraphrases with implicit rewriting patterns. Our experimental results on two real-world benchmark datasets demonstrate that our model generates at least one order of magnitude more diverse outputs than the baselines in terms of a new evaluation metric Jeffrey's Divergence. We have also conducted extensive experiments to understand various properties of our model with a focus on diversity.