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- W4385436491 abstract "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." @default.
- W4385436491 created "2023-08-01" @default.
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- W4385436491 date "2023-07-27" @default.
- W4385436491 modified "2023-10-18" @default.
- W4385436491 title "Multilingual Lexical Simplification via Paraphrase Generation" @default.
- W4385436491 doi "https://doi.org/10.48550/arxiv.2307.15286" @default.
- W4385436491 hasPublicationYear "2023" @default.
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