Paper ID: 2305.07824

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

Lingfeng Shen, Haiyun Jiang, Lemao Liu, Shuming Shi

Generating proper embedding of sentences through an unsupervised way is beneficial to semantic matching and retrieval problems in real-world scenarios. This paper presents Representation ALchemy (RepAL), an extremely simple post-processing method that enhances sentence representations. The basic idea in RepAL is to de-emphasize redundant information of sentence embedding generated by pre-trained models. Through comprehensive experiments, we show that RepAL is free of training and is a plug-and-play method that can be combined with most existing unsupervised sentence learning models. We also conducted in-depth analysis to understand RepAL.

Submitted: May 13, 2023