# LSBert: A Simple Framework for Lexical Simplification

arXiv: https://arxiv.org/abs/2006.14939 · HTML: https://mlsanity.com/paper/2006.14939/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification](https://mlsanity.com/paper/2302.02888/) (2023)
- [Lexical Simplification Benchmarks for English, Portuguese, and Spanish](https://mlsanity.com/paper/2209.05301/) (2022)
- [Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification](https://mlsanity.com/paper/2305.12463/) (2023)
- [On the Automatic Generation and Simplification of Children's Stories](https://mlsanity.com/paper/2310.18502/) (2023)
- [SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words](https://mlsanity.com/paper/2204.07779/) (2022)
- [A Novel Dataset for Financial Education Text Simplification in Spanish](https://mlsanity.com/paper/2312.09897/) (2023)
- [Lexical Complexity Prediction and Lexical Simplification for Catalan and Spanish: Resource Creation, Quality Assessment, and Ethical Considerations](https://mlsanity.com/paper/2404.07814/) (2024)
- [Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model](https://mlsanity.com/paper/2502.01090/) (2025)
- [Unsupervised Lexical Simplification with Context Augmentation](https://mlsanity.com/paper/2311.00310/) (2023)

## Abstract (arXiv)

Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning, to simplify the sentence. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. In this paper, we propose a lexical simplification framework LSBert based on pretrained representation model Bert, that is capable of (1) making use of the wider context when both detecting the words in need of simplification and generating substitue candidates, and (2) taking five high-quality features into account for ranking candidates, including Bert prediction order, Bert-based language model, and the paraphrase database PPDB, in addition to the word frequency and word similarity commonly used in other LS methods. We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and obtains obvious improvement compared with these baselines, outperforming the state-of-the-art by 29.8 Accuracy points on three well-known benchmarks.