# Lexical Simplification Benchmarks for English, Portuguese, and Spanish

arXiv: https://arxiv.org/abs/2209.05301 · HTML: https://mlsanity.com/paper/2209.05301/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 64 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [SemEval-2021 Task 1: Lexical Complexity Prediction](https://mlsanity.com/paper/2106.00473/) (2021)
- [LSBert: A Simple Framework for Lexical Simplification](https://mlsanity.com/paper/2006.14939/) (2020)
- [Chinese Lexical Simplification](https://mlsanity.com/paper/2010.07048/) (2020)

## Built on by (most cited first, 18 of 18)

- [BLESS: Benchmarking Large Language Models on Sentence Simplification](https://mlsanity.com/paper/2310.15773/) (2023)
- [Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification](https://mlsanity.com/paper/2302.02888/) (2023)
- [UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?](https://mlsanity.com/paper/2301.01764/) (2023)
- [Language Variety Identification with True Labels](https://mlsanity.com/paper/2303.01490/) (2023)
- [Exploring Large Language Models to generate Easy to Read content](https://mlsanity.com/paper/2407.20046/) (2024)
- [KidLM: Advancing Language Models for Children -- Early Insights and Future Directions](https://mlsanity.com/paper/2410.03884/) (2024)
- [MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders](https://mlsanity.com/paper/2212.09855/) (2022)
- [Japanese Lexical Complexity for Non-Native Readers: A New Dataset](https://mlsanity.com/paper/2306.17399/) (2023)
- [Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?](https://mlsanity.com/paper/2410.03240/) (2024)
- [ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification](https://mlsanity.com/paper/2209.09034/) (2022)
- [Creating a silver standard for patent simplification](https://mlsanity.com/paper/2310.15689/) (2023)
- [Controllable Lexical Simplification for English](https://mlsanity.com/paper/2302.02900/) (2023)
- [Multilingual Lexical Simplification via Paraphrase Generation](https://mlsanity.com/paper/2307.15286/) (2023)
- [A Novel Dataset for Financial Education Text Simplification in Spanish](https://mlsanity.com/paper/2312.09897/) (2023)
- [Automatic Text Simplification of News Articles in the Context of Public Broadcasting](https://mlsanity.com/paper/2212.13317/) (2022)
- [Lexical Simplification using multi level and modular approach](https://mlsanity.com/paper/2302.01823/) (2023)
- [Unsupervised Lexical Simplification with Context Augmentation](https://mlsanity.com/paper/2311.00310/) (2023)
- [Lexical Substitution is not Synonym Substitution: On the Importance of Producing Contextually Relevant Word Substitutes](https://mlsanity.com/paper/2502.04173/) (2025)

## Abstract (arXiv)

Even in highly-developed countries, as many as 15-30% of the population can only understand texts written using a basic vocabulary. Their understanding of everyday texts is limited, which prevents them from taking an active role in society and making informed decisions regarding healthcare, legal representation, or democratic choice. Lexical simplification is a natural language processing task that aims to make text understandable to everyone by replacing complex vocabulary and expressions with simpler ones, while preserving the original meaning. It has attracted considerable attention in the last 20 years, and fully automatic lexical simplification systems have been proposed for various languages. The main obstacle for the progress of the field is the absence of high-quality datasets for building and evaluating lexical simplification systems. We present a new benchmark dataset for lexical simplification in English, Spanish, and (Brazilian) Portuguese, and provide details about data selection and annotation procedures. This is the first dataset that offers a direct comparison of lexical simplification systems for three languages. To showcase the usability of the dataset, we adapt two state-of-the-art lexical simplification systems with differing architectures (neural vs. non-neural) to all three languages (English, Spanish, and Brazilian Portuguese) and evaluate their performances on our new dataset. For a fairer comparison, we use several evaluation measures which capture varied aspects of the systems' efficacy, and discuss their strengths and weaknesses. We find a state-of-the-art neural lexical simplification system outperforms a state-of-the-art non-neural lexical simplification system in all three languages. More importantly, we find that the state-of-the-art neural lexical simplification systems perform significantly better for English than for Spanish and Portuguese.