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.

基于预训练语言模型的词汇简化方法取得了显著进展，通过分析词语在其上下文环境中的替代词生成复杂词的潜在替代词。然而，这些方法需要针对不同语言进行单独的预训练模型，并且忽略了对句子意义的保留。本文提出了一种新颖的多语言词汇简化方法，通过生成释义来提供词语选择的多样性，同时保持句子的意义。我们将释义任务视为支持数百种语言的多语言神经机器翻译中的零 - shot 翻译任务。在释义建模的编码器中输入句子后，我们基于一种集中于复杂词的词汇变体的新颖解码策略生成替代词。实验结果表明，我们的方法在英语、西班牙语和葡萄牙语上明显优于基于 BERT 的方法和零 - shot GPT3 方法。