  The change point is a moment of an abrupt alteration in the data
distribution. Current methods for change point detection are based on recurrent
neural methods suitable for sequential data. However, recent works show that
transformers based on attention mechanisms perform better than standard
recurrent models for many tasks. The most benefit is noticeable in the case of
longer sequences. In this paper, we investigate different attentions for the
change point detection task and proposed specific form of attention related to
the task at hand. We show that using a special form of attention outperforms
state-of-the-art results.
