# Paraphrasing via Ranking Many Candidates

arXiv: https://arxiv.org/abs/2107.09274 · HTML: https://mlsanity.com/paper/2107.09274/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 18 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018)
- [CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling](https://mlsanity.com/paper/1811.10996/) (2018)
- [Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs](https://mlsanity.com/paper/2008.13546/) (2020)
- [BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection](https://mlsanity.com/paper/2005.12503/) (2020)
- [Unsupervised Paraphrasing by Simulated Annealing](https://mlsanity.com/paper/1909.03588/) (2019)
- [Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity](https://mlsanity.com/paper/2008.04935/) (2020)
- [Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator](https://mlsanity.com/paper/1806.00807/) (2018)

## Abstract (arXiv)

We present a simple and effective way to generate a variety of paraphrases and find a good quality paraphrase among them. As in previous studies, it is difficult to ensure that one generation method always generates the best paraphrase in various domains. Therefore, we focus on finding the best candidate from multiple candidates, rather than assuming that there is only one combination of generative models and decoding options. Our approach shows that it is easy to apply in various domains and has sufficiently good performance compared to previous methods. In addition, our approach can be used for data augmentation that extends the downstream corpus, showing that it can help improve performance in English and Korean datasets.