mlsanityData to 14 Oct 2026

Deep Learning Approaches to Lexical Simplification: A Survey

arXiv 2305.12000 ↗First posted 19 May 2023cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 93 references that are in the map. First posted on arXiv 19 May 2023.

Builds on

22 of 93 references.

References · 22

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  2. 2020Language Models are Few-Shot Learnerscited by 32,419
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  11. 2018A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplificationcited by 24
  12. 2020CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Datacited by 24
10 more
  1. 2015sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddingscited by 20
  2. 2023Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplificationcited by 20
  3. 2021SemEval-2021 Task 1: Lexical Complexity Predictioncited by 19
  4. 2022Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitutioncited by 12
  5. 2023UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?cited by 12
  6. 2022One Size Does Not Fit All: The Case for Personalised Word Complexity Modelscited by 10
  7. 2020Chinese Lexical Simplificationcited by 8
  8. 2022MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoderscited by 5
  9. 2021One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languagescited by 4
  10. 2023Lexical Complexity Prediction: An Overviewcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservationcited by 5
  2. 2024Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learningcited by 1
  3. 2026RALS: Resources and Baselines for Romanian Automatic Lexical Simplificationcited by 1
  4. 2026Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplificationcited by 0