An Extensible Massively Multilingual Lexical Simplification Pipeline Dataset using the MultiLS Framework

Shardlow, Matthew [Manchester Metropolitan University] Alva-Manchego, Fernando [Cardiff University] Batista-Navarro, Riza [University of Manchester] Bott, Stefan [Universitat Pompeu Fabra] Calderon Ramirez, Saul [Tecnológico de Costa Rica] Cardon, Rémi [UCL] François, Thomas [UCL] Hayakawa, Akio [Universitat Pompeu Fabra] Horbach, Andrea [University of Hildesheim] Hülsing, Anna [University of Hildesheim] Ide, Yusuke [NARA Institute of Science and Technology] Marvin Imperial, Joseph [National University Philippines] Nohejl, Adam [NARA Institute of Science and Technology] North, Kai [George Mason University] Occhipinti, Laura [University of Bologna] Peréz Rojas, Nelson [Tecnológico de Costa Rica] Raihan, Nishat [George Mason University] Ranasinghe, Tharindu [Aston University] Solis Salazar, Martin [Tecnológico de Costa Rica] Zampieri, Marcos [George Mason University] Saggion, Horacio [Universitat Pompeu Fabra] et al.[show all]

We present preliminary findings on the MultiLS dataset, developed in support of the 2024 Multilingual Lexical Simplification Pipeline (MLSP) Shared Task. This dataset currently comprises of 300 instances of lexical complexity prediction and lexical simplification across 10 languages. In this paper, we (1) describe the annotation protocol in support of the contribution of future datasets and (2) present summary statistics on the existing data that we have gathered. Multilingual lexical simplification can be used to support low-ability readers to engage with otherwise difficult texts in their native, often low-resourced, languages.