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Lexical substitution (LS) aims at finding appropriate substitutes for atarget word in a sentence. Recently, LS methods based on pretrained languagemodels have made remarkable progress, generating potential substitutes for atarget word through analysis of its contextual surroundings. However, thesemethods tend to overlook the preservation of the sentence's meaning whengenerating the substitutes. This study explores how to generate the substitutecandidates from a paraphraser, as the generated paraphrases from a paraphrasercontain variations in word choice and preserve the sentence's meaning. Since wecannot directly generate the substitutes via commonly used decoding strategies,we propose two simple decoding strategies that focus on the variations of thetarget word during decoding. Experimental results show that our methodsoutperform state-of-the-art LS methods based on pre-trained language models onthree benchmarks.

ParaLS: Lexical Substitution via Pretrained Paraphraser