North, Kai and Ranasinghe, Tharindu and Shardlow, Matthew and Zampieri, Marcos (2024) MultiLS : An End-to-End Lexical Simplification Framework. In: Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024) :. Association for Computational Linguistics (ACL Anthology), Kerrville, pp. 1-11. ISBN 9798891761766
Full text not available from this repository.Abstract
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.