  Pre-training models such as BERT have achieved great success in many natural
language processing tasks. However, how to obtain better sentence
representation through these pre-training models is still worthy to exploit.
Previous work has shown that the anisotropy problem is an critical bottleneck
for BERT-based sentence representation which hinders the model to fully utilize
the underlying semantic features. Therefore, some attempts of boosting the
isotropy of sentence distribution, such as flow-based model, have been applied
to sentence representations and achieved some improvement. In this paper, we
find that the whitening operation in traditional machine learning can similarly
enhance the isotropy of sentence representations and achieve competitive
results. Furthermore, the whitening technique is also capable of reducing the
dimensionality of the sentence representation. Our experimental results show
that it can not only achieve promising performance but also significantly
reduce the storage cost and accelerate the model retrieval speed.

Computation and Language;Artificial Intelligence;Machine Learning