MultiLS: An End-to-End Lexical Simplification Framework

• DOI: 10.18653/v1/2024.tsar-1.1
• Publication Date: 2024-11-27T22:28:12Z
• Authors: Kai North; Tharindu Ranasinghe; Matthew Shardlow; Marcos Zampieri

Lexical Simplification (LS) automatically replaces difficult to read words for easier alternatives while preserving a sentence’s original meaning. Several datasets exist for LS and each of them specialize in one or two sub-tasks within the LS pipeline. However, as of this moment, no single LS dataset has been developed that covers all LS sub-tasks. We present MultiLS, the first LS framework that allows for the creation of a multi-task LS dataset. We also present MultiLS-PT, the first dataset created using the MultiLS framework. We demonstrate the potential of MultiLS-PT by carrying out all LS sub-tasks of (1) lexical complexity prediction (LCP), (2) substitute generation, and (3) substitute ranking for Portuguese.