RCML at TSAR -2022 Shared Task: Lexical Simplification With Modular Substitution Candidate Ranking (2022)

Abstract: This paper describes the lexical simplification system RCML submitted to the English language track of the TSAR-2022 Shared Task. The system leverages a pre-trained language model to generate contextually plausible substitution candidates which are then ranked according to their simplicity as well as their grammatical and semantic similarity to the target complex word. Our submissions secure 6th and 7th places out of 33, improving over the SOTA baseline for 27 out of the 51 metrics.

## introduction lexical simplification (ls) is a means to facilitate reading comprehension for different target audiences such as second language learners, native speakers with low literacy levels or various kinds of neurodivergent conditions and reading impairments. ## system description we propose a modular system for lexical simplification which requires no training data and allows to fine-tune each module separately in order to improve the final result. since the dataset already provides complex word annotations, rcml is composed of only two modules: one for candidate generation and one for candidate ranking. ## results and discussion we submitted results from two runs of the same system with the only difference being the grammaticality score. in our second submission, we disabled the filtering of morphologically inconsistent substitution candidates (in other words, we assign g w a score of 1 for all w) after noticing that in some cases, some very appropriate candidates get filtered out following an erroneous morphological analysis. both submissions achieve very similar results @xcite , but the second one improves on the first on all but two metrics: acc@1@top1 and acc@3@top1 despite generating inappropriate candidates (both semantically and syntactically) in some rare cases. 1 shows the top of the leaderboard including our team's two submissions. rcml has potential@3 of 0.831 which is higher than lsbert's @xcite and comparable to the top-scoring systems. this result suggests a potential for our system to assist human editors in the task of lexical simplification by proposing a few simpler synonyms to choose from. the system's accuracy@1@top1 doubles when @xmath0, the most commonly suggested substitute is among our top 3 predictions. ## conclusion in this paper, we describe a modular lexical simplification system for english which requires no training data. rcml uses lexsubgen to generate substitution candidates before evaluating their grammaticality, meaning and simplicity. the latter is predicted by a 6-class contextual cefr vocabulary classifier. the system is easily adaptable to other languages provided a trained cefr vocabulary classifier in the languages in question. it also has the capacity to perform personalized lexical simplification, a particularly relevant approach when simplifying text for language learners at different proficiency levels.