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.

翻译：文本简化是自然语言处理领域的一个持续性难题，其解决方案具有多重意义。结合TSAR-2022研讨会@EMNLP2022，词汇简化是指通过用更易读（或理解）的表达替换困难词汇来降低文本词汇复杂度，同时保留原始信息和含义。本文阐述了我们的"teamPN"团队在英语子任务中的工作成果。我们构建了一个模块化流水线，将现代基于Transformer的模型与释义、动词词义消歧等传统自然语言处理方法相结合。我们创建了多层级模块化流水线，根据目标文本的语义（词性标注）对其进行处理。该流水线具有多层级性，因为我们利用多个源模型寻找候选替换词；同时具有模块化特性，因为我们可以灵活切换源模型及其在最终重排序中的权重比例。