teamPN at TSAR-2022 Shared Task: Lexical Simplification using Multi-Level and Modular Approach

Nikita Nikita, P. Rajpoot
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引用次数: 2

Abstract

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 the English track of TSAR 2022 Shared Task of Lexical Simplification. We created a modular pipeline which combines 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). The 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 weighting in the final re-ranking.
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teamPN在TSAR-2022共享任务:使用多层次和模块化方法的词汇简化
词汇简化是在保留原文信息和意思的情况下,用容易阅读(或理解)的表达代替难理解的单词,从而降低文本词汇复杂性的过程。本文介绍了我们团队“teamPN”为TSAR 2022词汇简化共享任务的英语轨道所做的工作。我们创建了一个模块化的管道,将基于变形器的模型与传统的NLP方法(如释义和动词语义消歧)相结合。我们创建了一个多级和模块化的管道,其中目标文本根据其语义(Part of Speech Tag)进行处理。管道是多层次的,因为我们利用多个源模型来寻找潜在的替代候选。它是模块化的,因为我们可以在最终的重新排序中切换源模型和它们的权重。
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