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Generating proper embedding of sentences through an unsupervised way isbeneficial to semantic matching and retrieval problems in real-world scenarios.This paper presents Representation ALchemy (RepAL), an extremely simplepost-processing method that enhances sentence representations. The basic ideain RepAL is to de-emphasize redundant information of sentence embeddinggenerated by pre-trained models. Through comprehensive experiments, we showthat RepAL is free of training and is a plug-and-play method that can becombined with most existing unsupervised sentence learning models. We alsoconducted in-depth analysis to understand RepAL.

A Simple and Plug-and-play Method for Unsupervised Sentence Representation Enhancement