Lexical Simplification using multi level and modular approach

TL;DR

This paper presents a multi-level, modular lexical simplification pipeline that combines transformer models with traditional NLP techniques to improve text readability while preserving meaning.

Contribution

It introduces a novel multi-level, modular approach integrating modern transformers with traditional NLP methods for lexical simplification.

Findings

Effective combination of transformer models and traditional NLP techniques.

Flexible modular pipeline allows switching source models and adjusting their influence.

Improved lexical simplification performance demonstrated.

Abstract

Text Simplification is an ongoing problem in Natural Language Processing, solution to which has varied implications. In conjunction with the TSAR-2022 Workshop @EMNLP2022 Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier to read (or understand) expressions while preserving the original information and meaning. This paper explains the work done by our team "teamPN" for English sub task. We created a modular pipeline which combines modern day transformers based models with traditional NLP methods like paraphrasing and verb sense disambiguation. We created a multi level and modular pipeline where the target text is treated according to its semantics(Part of Speech Tag). Pipeline is multi level as we utilize multiple source models to find potential candidates for replacement, It is modular as we can switch the source models and their weight-age in the final re-ranking.