Personalizing Lexical Simplification

Given an input text from the user, a lexical simplification (LS) system makes the text easier to understand by substituting difficult words with simpler words. The best substitution may vary from one user to another, given individual differences in vocabulary proficiency level. Most current systems, however, do not consider these variations, and are instead trained to find one optimal substitution or list of substitutions for all users. This paper measures the benefits of using complex word identification (CWI) models to personalize an LS system. Experimental results show that even a simple CWI model, based on graded vocabulary lists, can help reduce the number of unnecessary simplifications and complex words in the output for learners of English at different proficiency levels.