Deep Learning Approaches to Lexical Simplification: A Survey

• URL: http://arxiv.org/abs/2305.12000v1
• Date: Fri, 19 May 2023 20:56:22 GMT
• Title: Deep Learning Approaches to Lexical Simplification: A Survey
• Authors: Kai North, Tharindu Ranasinghe, Matthew Shardlow, Marcos Zampieri
• Abstract summary: Lexical Simplification (LS) is the task of replacing complex for simpler words in a sentence. LS is the lexical component of Text Simplification (TS) Recent advances in deep learning have sparked renewed interest in LS.
• Score: 19.079916794185642
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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