  Lexical simplification (LS) methods based on pretrained language models have
made remarkable progress, generating potential substitutes for a complex word
through analysis of its contextual surroundings. However, these methods require
separate pretrained models for different languages and disregard the
preservation of sentence meaning. In this paper, we propose a novel
multilingual LS method via paraphrase generation, as paraphrases provide
diversity in word selection while preserving the sentence's meaning. We regard
paraphrasing as a zero-shot translation task within multilingual neural machine
translation that supports hundreds of languages. After feeding the input
sentence into the encoder of paraphrase modeling, we generate the substitutes
based on a novel decoding strategy that concentrates solely on the lexical
variations of the complex word. Experimental results demonstrate that our
approach surpasses BERT-based methods and zero-shot GPT3-based method
significantly on English, Spanish, and Portuguese.
