# Sentence Simplification with Memory-Augmented Neural Networks

arXiv: https://arxiv.org/abs/1804.07445 · HTML: https://mlsanity.com/paper/1804.07445/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 10 of 10)

- [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)
- [Multitasking Framework for Unsupervised Simple Definition Generation](https://mlsanity.com/paper/2203.12926/) (2022)
- [The SAMER Arabic Text Simplification Corpus](https://mlsanity.com/paper/2404.18615/) (2024)
- [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)
- [Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservation](https://mlsanity.com/paper/2404.03278/) (2024)
- [SiTSE: Sinhala Text Simplification Dataset and Evaluation](https://mlsanity.com/paper/2412.01293/) (2024)
- [KnowAct-GUIClaw: Know Deeply, Act Perfectly, Personal GUI Assistant with Self-Evolving Memory and Skill](https://mlsanity.com/paper/2607.12625/) (2026)
- [Noisy Channel for Automatic Text Simplification](https://mlsanity.com/paper/2211.03152/) (2022)

## Abstract (arXiv)

Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in neural machine translation have paved the way for novel approaches to the task. In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.