ParaLS: Lexical Substitution via Pretrained Paraphraser
Jipeng Qiang Kang Liu Yun Li Yunhao Yuan Yi Zhu
Lexical substitution (LS) aims at finding appropriate substitutes for a
target word in a sentence. Recently, LS methods based on pretrained language
models have made remarkable progress, generating potential substitutes for a
target word through analysis of its contextual surroundings. However, these
methods tend to overlook the preservation of the sentence's meaning when
generating the substitutes. This study explores how to generate the substitute
candidates from a paraphraser, as the generated paraphrases from a paraphraser
contain variations in word choice and preserve the sentence's meaning. Since we
cannot directly generate the substitutes via commonly used decoding strategies,
we propose two simple decoding strategies that focus on the variations of the
target word during decoding. Experimental results show that our methods
outperform state-of-the-art LS methods based on pre-trained language models on
three benchmarks.