Enhancing SimpLex, a Lexical Simplification Architecture

Lexical simplification (LS) is the process of replacing complex words in a sentence with simpler alternatives to improve readability and understandability. However, LS is a challenging task because the target population may have varying literacy levels, resulting in different needs. This study aims to enhance the SimpLex architecture, a LS framework proposed in the literature. Several techniques have been explored to achieve this goal. First, we added new features and trained two additional machine learning models: LightGBM and XGBoost. Additionally, multiple transformer models were examined, including DistilBERT, RoBERTa Large, Facebook BART, and Microsoft DeBERTa. The results demonstrated the effectiveness of LightGBM and XGBoost in predicting complex words. Furthermore, DistilBERT and RoBERTa Large achieved higher SARI scores and lower perplexity compared to the other pre-trained models, except BERT, which had a slightly lower perplexity than the aforementioned models.