CILS at TSAR-2022 Shared Task: Investigating the Applicability of Lexical Substitution Methods for Lexical Simplification

Abstract

Lexical simplification — which aims to simplify complex text through the replacement of difficult words using simpler alternatives while maintaining the meaning of the given text — is popular as a way of improving text accessibility for both people and computers. First, lexical simplification through substitution can improve the understandability of complex text for, for example, non-native speakers, second language learners, and people with low literacy. Second, its usefulness has been demonstrated in many natural language processing problems like data augmentation, paraphrase generation, or word sense induction. In this paper, we investigated the applicability of existing unsupervised lexical substitution methods based on pre-trained contextual embedding models and WordNet, which incorporate Context Information, for Lexical Simplification (CILS). Although the performance of this CILS approach has been outstanding in lexical substitution tasks, its usefulness was limited at the TSAR-2022 shared task on lexical simplification. Consequently, a minimally supervised approach with careful tuning to a given simplification task may work better than unsupervised methods. Our investigation also encouraged further work on evaluating the simplicity of potential candidates and incorporating them into the lexical simplification methods.