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.