# Controllable Lexical Simplification for English

arXiv: https://arxiv.org/abs/2302.02900 · HTML: https://mlsanity.com/paper/2302.02900/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [Lexical Simplification Benchmarks for English, Portuguese, and Spanish](https://mlsanity.com/paper/2209.05301/) (2022)

## Built on by (most cited first, 2 of 2)

- [The SAMER Arabic Text Simplification Corpus](https://mlsanity.com/paper/2404.18615/) (2024)
- [Multilingual Controllable Transformer-Based Lexical Simplification](https://mlsanity.com/paper/2307.02120/) (2023)

## Abstract (arXiv)

Fine-tuning Transformer-based approaches have recently shown exciting results on sentence simplification task. However, so far, no research has applied similar approaches to the Lexical Simplification (LS) task. In this paper, we present ConLS, a Controllable Lexical Simplification system fine-tuned with T5 (a Transformer-based model pre-trained with a BERT-style approach and several other tasks). The evaluation results on three datasets (LexMTurk, BenchLS, and NNSeval) have shown that our model performs comparable to LSBert (the current state-of-the-art) and even outperforms it in some cases. We also conducted a detailed comparison on the effectiveness of control tokens to give a clear view of how each token contributes to the model.