We propose a new unsupervised method for lexical substitution using
pre-trained language models. Compared to previous approaches that use the
generative capability of language models to predict substitutes, our method
retrieves substitutes based on the similarity of contextualised and
decontextualised word embeddings, i.e. the average contextual representation of
a word in multiple contexts. We conduct experiments in English and Italian, and
show that our method substantially outperforms strong baselines and establishes
a new state-of-the-art without any explicit supervision or fine-tuning. We
further show that our method performs particularly well at predicting
low-frequency substitutes, and also generates a diverse list of substitute
candidates, reducing morphophonetic or morphosyntactic biases induced by
article-noun agreement.

本文采用预训练语言模型提出一种新的无监督词汇替换方法，通过根据单词在多个上下文中的平均上下文表示相似性来检索替代词，实验表明该方法在英语和意大利语上表现明显优于强基线，并且能够成功预测低频替代词且不受形态和句法中的词汇一致性的影响。