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.

本文介绍了我们队伍 “teamPN” 为英语子任务所做的工作，我们创建了一个模块化的流水线，将现代转换器模型与传统的 NLP 方法相结合，创建了一个多级和模块化的系统，用于根据词性标注处理目标文本，以实现文本简化的目标。