MultiLS: A Multi-task Lexical Simplification Framework
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. LS is a
precursor to Text Simplification with the aim of improving text accessibility
to various target demographics, including children, second language learners,
individuals with reading disabilities or low literacy. Several datasets exist
for LS. These LS datasets specialize on 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 to be 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. Model performances are reported, ranging from
transformer-based models to more recent large language models (LLMs).