MultiLS: An End-to-End Lexical Simplification Framework
Kai North, Tharindu Ranasinghe, Matthew Shardlow, Marcos Zampieri · 2024
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 Mul-tiLS, 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.