  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.
