# Unsupervised Paraphrasing by Simulated Annealing

arXiv: https://arxiv.org/abs/1909.03588 · HTML: https://mlsanity.com/paper/1909.03588/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2019.

## Built on by (most cited first, 30 of 37)

- [Generating Sequences by Learning to Self-Correct](https://mlsanity.com/paper/2211.00053/) (2022)
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- [On the Evaluation Metrics for Paraphrase Generation](https://mlsanity.com/paper/2202.08479/) (2022)
- [Quality Controlled Paraphrase Generation](https://mlsanity.com/paper/2203.10940/) (2022)
- [Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning](https://mlsanity.com/paper/2202.00535/) (2022)
- [ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning](https://mlsanity.com/paper/2105.12995/) (2021)
- [Text Revision by On-the-Fly Representation Optimization](https://mlsanity.com/paper/2204.07359/) (2022)
- [Prompt-Based Editing for Text Style Transfer](https://mlsanity.com/paper/2301.11997/) (2023)
- [Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing](https://mlsanity.com/paper/2112.06924/) (2021)
- [ConRPG: Paraphrase Generation using Contexts as Regularizer](https://mlsanity.com/paper/2109.00363/) (2021)
- [Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization](https://mlsanity.com/paper/2205.14521/) (2022)
- [Teacher Forcing Recovers Reward Functions for Text Generation](https://mlsanity.com/paper/2210.08708/) (2022)
- [Quantum Natural Language Generation on Near-Term Devices](https://mlsanity.com/paper/2211.00727/) (2022)
- [Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning](https://mlsanity.com/paper/2103.12777/) (2021)
- [Generic resources are what you need: Style transfer tasks without task-specific parallel training data](https://mlsanity.com/paper/2109.04543/) (2021)
- [Search and Learn: Improving Semantic Coverage for Data-to-Text Generation](https://mlsanity.com/paper/2112.02770/) (2021)
- [Unsupervised Editing for Counterfactual Stories](https://mlsanity.com/paper/2112.05417/) (2021)
- [Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach](https://mlsanity.com/paper/2109.01862/) (2021)
- [Controlling Text Edition by Changing Answers of Specific Questions](https://mlsanity.com/paper/2105.11018/) (2021)
- [Simulated annealing for optimization of graphs and sequences](https://mlsanity.com/paper/2110.01384/) (2021)
- [Simulated Annealing for Emotional Dialogue Systems](https://mlsanity.com/paper/2109.10715/) (2021)
- [Learning to Selectively Learn for Weakly-supervised Paraphrase Generation](https://mlsanity.com/paper/2109.12457/) (2021)
- [Entailment Relation Aware Paraphrase Generation](https://mlsanity.com/paper/2203.10483/) (2022)
- [Syntactically Robust Training on Partially-Observed Data for Open Information Extraction](https://mlsanity.com/paper/2301.06841/) (2023)
- [Weakly Supervised Reasoning by Neuro-Symbolic Approaches](https://mlsanity.com/paper/2309.13072/) (2023)
- [The Emergence of Chunking Structures with Hierarchical RNN](https://mlsanity.com/paper/2309.04919/) (2023)
- [Learning to Adapt to Low-Resource Paraphrase Generation](https://mlsanity.com/paper/2412.17111/) (2024)
- ['John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems](https://mlsanity.com/paper/2206.08263/) (2022)
- [Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising](https://mlsanity.com/paper/2302.02780/) (2023)

## Abstract (arXiv)

Unsupervised paraphrase generation is a promising and important research topic in natural language processing. We propose UPSA, a novel approach that accomplishes Unsupervised Paraphrasing by Simulated Annealing. We model paraphrase generation as an optimization problem and propose a sophisticated objective function, involving semantic similarity, expression diversity, and language fluency of paraphrases. Then, UPSA searches the sentence space towards this objective by performing a sequence of local editing. Our method is unsupervised and does not require parallel corpora for training, so it could be easily applied to different domains. We evaluate our approach on a variety of benchmark datasets, namely, Quora, Wikianswers, MSCOCO, and Twitter. Extensive results show that UPSA achieves the state-of-the-art performance compared with previous unsupervised methods in terms of both automatic and human evaluations. Further, our approach outperforms most existing domain-adapted supervised models, showing the generalizability of UPSA.