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arXiv · 2305.07824

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

Lingfeng Shen·Haiyun Jiang·Lemao Liu·Shuming Shi
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Abstract

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.

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Lingfeng Shen, Haiyun Jiang, Lemao Liu, Shuming Shi. 2023-05-13. A Simple and Plug-and-play Method for Unsupervised Sentence Representation Enhancement. https://arxiv.org/abs/2305.07824

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