# Integrating Transformer and Paraphrase Rules for Sentence Simplification

arXiv: https://arxiv.org/abs/1810.11193 · HTML: https://mlsanity.com/paper/1810.11193/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 29 of 29)

- [Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs](https://mlsanity.com/paper/2101.10579/) (2021)
- [EditEval: An Instruction-Based Benchmark for Text Improvements](https://mlsanity.com/paper/2209.13331/) (2022)
- [Text Simplification by Tagging](https://mlsanity.com/paper/2103.05070/) (2021)
- [Document-Level Text Simplification: Dataset, Criteria and Baseline](https://mlsanity.com/paper/2110.05071/) (2021)
- [Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning](https://mlsanity.com/paper/2202.00535/) (2022)
- [Rethinking Automatic Evaluation in Sentence Simplification](https://mlsanity.com/paper/2104.07560/) (2021)
- [Sentence Simplification via Large Language Models](https://mlsanity.com/paper/2302.11957/) (2023)
- [An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages](https://mlsanity.com/paper/2109.00165/) (2021)
- [Paraphrase Identification with Deep Learning: A Review of Datasets and Methods](https://mlsanity.com/paper/2212.06933/) (2022)
- [How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks](https://mlsanity.com/paper/2109.04604/) (2021)
- [The SAMER Arabic Text Simplification Corpus](https://mlsanity.com/paper/2404.18615/) (2024)
- [GRS: Combining Generation and Revision in Unsupervised Sentence Simplification](https://mlsanity.com/paper/2203.09742/) (2022)
- [XATU: A Fine-grained Instruction-based Benchmark for Explainable Text Updates](https://mlsanity.com/paper/2309.11063/) (2023)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Investigating Text Simplification Evaluation](https://mlsanity.com/paper/2107.13662/) (2021)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [An In-depth Evaluation of Large Language Models in Sentence Simplification with Error-based Human Assessment](https://mlsanity.com/paper/2403.04963/) (2024)
- [Text Simplification for Comprehension-based Question-Answering](https://mlsanity.com/paper/2109.13984/) (2021)
- [Science Out of Its Ivory Tower: Improving Accessibility with Reinforcement Learning](https://mlsanity.com/paper/2410.17088/) (2024)
- [SiTSE: Sinhala Text Simplification Dataset and Evaluation](https://mlsanity.com/paper/2412.01293/) (2024)
- [Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition](https://mlsanity.com/paper/2502.11457/) (2025)
- [Cognitive Simplification Operations Improve Text Simplification](https://mlsanity.com/paper/2211.08825/) (2022)
- [Explain to me like I am five -- Sentence Simplification Using Transformers](https://mlsanity.com/paper/2212.04595/) (2022)
- [Simplifying Scholarly Abstracts for Accessible Digital Libraries](https://mlsanity.com/paper/2408.03899/) (2024)
- [Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets](https://mlsanity.com/paper/2501.15624/) (2025)
- [An Empirical Study on Learning and Improving the Search Objective for Unsupervised Paraphrasing](https://mlsanity.com/paper/2203.12106/) (2022)
- [Noisy Channel for Automatic Text Simplification](https://mlsanity.com/paper/2211.03152/) (2022)
- [Can Knowledge Graphs Simplify Text?](https://mlsanity.com/paper/2308.06975/) (2023)
- [Action Controlled Paraphrasing](https://mlsanity.com/paper/2405.11277/) (2024)

## Abstract (arXiv)

Sentence simplification aims to reduce the complexity of a sentence while retaining its original meaning. Current models for sentence simplification adopted ideas from ma- chine translation studies and implicitly learned simplification mapping rules from normal- simple sentence pairs. In this paper, we explore a novel model based on a multi-layer and multi-head attention architecture and we pro- pose two innovative approaches to integrate the Simple PPDB (A Paraphrase Database for Simplification), an external paraphrase knowledge base for simplification that covers a wide range of real-world simplification rules. The experiments show that the integration provides two major benefits: (1) the integrated model outperforms multiple state- of-the-art baseline models for sentence simplification in the literature (2) through analysis of the rule utilization, the model seeks to select more accurate simplification rules. The code and models used in the paper are available at https://github.com/ Sanqiang/text_simplification.