Deep Learning Approaches to Lexical Simplification: A Survey

TL;DR

This survey reviews recent advances in deep learning for Lexical Simplification, highlighting new models, datasets, and the impact of large language models on making text more accessible.

Contribution

It provides a comprehensive overview of deep learning methods for LS from 2017 to 2023 and introduces benchmark datasets for future research.

Findings

Deep learning has significantly improved LS performance.

Large language models show promising results in LS tasks.

Benchmark datasets are now available for standardized evaluation.

Abstract

Lexical Simplification (LS) is the task of replacing complex for simpler words in a sentence whilst preserving the sentence's original meaning. LS is the lexical component of Text Simplification (TS) with the aim of making texts more accessible to various target populations. A past survey (Paetzold and Specia, 2017) has provided a detailed overview of LS. Since this survey, however, the AI/NLP community has been taken by storm by recent advances in deep learning, particularly with the introduction of large language models (LLM) and prompt learning. The high performance of these models sparked renewed interest in LS. To reflect these recent advances, we present a comprehensive survey of papers published between 2017 and 2023 on LS and its sub-tasks with a special focus on deep learning. We also present benchmark datasets for the future development of LS systems.