# Aligning Sentence Simplification with ESL Learner's Proficiency for Language Acquisition

arXiv: https://arxiv.org/abs/2502.11457 · HTML: https://mlsanity.com/paper/2502.11457/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 49 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 27 of 27)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [FUDGE: Controlled Text Generation With Future Discriminators](https://mlsanity.com/paper/2104.05218/) (2021)
- [NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://mlsanity.com/paper/2112.08726/) (2021)
- [NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints](https://mlsanity.com/paper/2010.12884/) (2020)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing](https://mlsanity.com/paper/1906.08104/) (2019)
- [LENS: A Learnable Evaluation Metric for Text Simplification](https://mlsanity.com/paper/2212.09739/) (2022)
- [EASSE: Easier Automatic Sentence Simplification Evaluation](https://mlsanity.com/paper/1908.04567/) (2019)
- [Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text](https://mlsanity.com/paper/2107.03444/) (2021)
- [ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness](https://mlsanity.com/paper/2305.12947/) (2023)
- [BLESS: Benchmarking Large Language Models on Sentence Simplification](https://mlsanity.com/paper/2310.15773/) (2023)
- [Iterative Edit-Based Unsupervised Sentence Simplification](https://mlsanity.com/paper/2006.09639/) (2020)
- [PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding](https://mlsanity.com/paper/2109.13582/) (2021)
- [Semantic Structural Evaluation for Text Simplification](https://mlsanity.com/paper/1810.05022/) (2018)
- [Integrating Transformer and Paraphrase Rules for Sentence Simplification](https://mlsanity.com/paper/1810.11193/) (2018)
- [Document-Level Text Simplification: Dataset, Criteria and Baseline](https://mlsanity.com/paper/2110.05071/) (2021)
- [Dynamic Multi-Level Multi-Task Learning for Sentence Simplification](https://mlsanity.com/paper/1806.07304/) (2018)
- [Scalable Online Planning via Reinforcement Learning Fine-Tuning](https://mlsanity.com/paper/2109.15316/) (2021)
- [Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA](https://mlsanity.com/paper/2305.14458/) (2023)
- [Controlling Pre-trained Language Models for Grade-Specific Text Simplification](https://mlsanity.com/paper/2305.14993/) (2023)
- [DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification](https://mlsanity.com/paper/2305.18939/) (2023)
- [Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification](https://mlsanity.com/paper/2310.08170/) (2023)
- [Learning to Paraphrase Sentences to Different Complexity Levels](https://mlsanity.com/paper/2308.02226/) (2023)

## Built on by (most cited first, 3 of 3)

- [Policy-based Sentence Simplification: Replacing Parallel Corpora with LLM-as-a-Judge](https://mlsanity.com/paper/2512.06228/) (2025)
- [Template-Based Text-to-Image Alignment for Language Accessibility: A Study on Visualizing Text Simplifications](https://mlsanity.com/paper/2510.11314/) (2025)
- [Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification](https://mlsanity.com/paper/2604.05302/) (2026)

## Abstract (arXiv)

Text simplification is crucial for improving accessibility and comprehension for English as a Second Language (ESL) learners. This study goes a step further and aims to facilitate ESL learners' language acquisition by simplification. Specifically, we propose simplifying complex sentences to appropriate levels for learners while also increasing vocabulary coverage of the target level in the simplifications. We achieve this without a parallel corpus by conducting reinforcement learning on a large language model. Our method employs token-level and sentence-level rewards, and iteratively trains the model on its self-generated outputs to guide the model to search for simplification hypotheses that satisfy the target attributes. Experiment results on CEFR-SP and TurkCorpus datasets show that the proposed method can effectively increase the frequency and diversity of vocabulary of the target level by more than 20% compared to baseline models, while maintaining high simplification quality.